Talk
!!!!%color=#449944% '''Colin MacKenzie'''
Simultaneous localization and mapping (SLAM) using side-scan sonar on-board autonomous underwater vehicles (AUV) for mapping mine-like objects (or targets) was implemented and tested in- water. The work is motivated by persistent SLAM for change detection over longer periods.
The payload autonomy was implemented in MOOS-IvP on-board the backseat processor of the IVER AUV. iSAM (incremental smoothing and mapping) was the SLAM engine used for the core state estimation. The DRDC automated target detection (ATD) tools were used to perform the on-line target detection and identification as well as provide an estimate for the target location. The data association used was joint compatibility branch and bound. These tools and algorithms were integrated and/or developed with a hardware-in-the loop simulator for the Iver AUV. Newly developed MOOS modules are discussed.
Three sets of in-water trials, separated by weeks, were performed to collect the change detection data sets. A year later a newer data set was collected over the same area with the AUV fusing a high calibre INS into the dead-reckoned navigation solution. The presentation discusses the use of the MOOS framework for implementing in-water SLAM using the DRDC ATD tools and iSAM on an embedded processor. As a measure of localization accuracy, the internal dead-reckoned AUV position estimate (with and without INS) just before surfacing is compared with the GPS fix on the surface and with the SLAM position estimate.
As maritime autonomy is introduced to perform increasingly complex missions, there is a prevailing need to autonomously determine complex event sequences and task schedules, as well as visualizing those vehicle actions before a mission. The Naval Surface Warfare Center, Panama City Division (NSWC PCD) has developed interfaces between MOOS-IvP to aid with both areas. We present a software framework to interface the commercial off-the-shelf optimization software OPL/CPLEX with MOOS-IvP. IBM's OPL/CPLEX is one of the world’s fastest optimization software packages for solving numerical optimization problems such as mixed-integer linear programming, quadratic programming, and scheduling. The OPL-MOOS interface will allow unmanned systems controlled by the MOOS-IvP autonomy framework to utilize numerical optimization routines found in OPL/CPLEX for autonomous plan optimization and scheduling. For mission visualization, we present an overview of a novel three-dimensional mission visualization software package developed by NSWC PCD, and its interface with MOOS-IvP using Google protocol buffers. We believe the two interfaces and software packages allow for extending the possible mission profiles and tasking with which MOOS-IvP can be used.
Autonomous underwater vehicles (AUVs) are regularly used as platforms for side scan sonar systems. One particularly common use is in the detection, identification and classification of underwater mines; known as mine countermeasures (MCM). Traditionally the development of processing algorithms for this task is performed iteratively; that is, a vehicle is taken out to an example mine field, either real or populated with training mines, where a selection of scans are collected. The vehicle simply records the collected data including navigational information which is then processed back at the “office” to train and improve the algorithms. When as much as possible has been gained from the latest set of data another trial is run, hopefully under different conditions so as to be useful in training.
Since changes in the ocean floor are limited over reasonable time scales and continually laying new realistic synthetic mine fields is difficult groups are forced to take their vehicles further and further on field trips at great expense; both financially and in lost development time. In collaboration with the Australian Defence Science and Technology Organisation (DSTO) we have developed a high fidelity, highly configurable real time side scan sonar simulator such that virtual trials in variable mine fields can facilitate rapid iterative development.
Our simulator integrates with a MOOS simulation enabling complete missions to be run including all autonomy systems, acoustic communication and vehicle dynamics and control. The MOOS simulator provides regular updates on the vehicles real world position as well as its internal position estimate, which our simulator uses in combination with a high fidelity 3D mesh of the environment to provide simulated side scan sonar output, including estimated position information for each pulse. The side scan simulator itself uses the NVIDIA OptiX ray tracing framework to perform the core of the simulation on any available CUDA enabled compute card(s). In this presentation we present the simulator as applied particularly to a REMUS 100 vehicle, as that is the primary vehicle in use by DSTO for side scan based MCM.
!!!!%color=#449944% '''David G. Johnson, Australian Centre for Field Robotics, The University of Sydney''
!!!!%color=#449944% ''' David Battle, Defence Science and Technology Organisation'''
* Vehicle Safety
* Bluefin Robotics
[[Path:/moos-dawg13/docs/01-schneider_brief.pdf | %color=#ff7f00% DOWNLOAD]]
The NATO Undersea Research Centre is currently exploring system concepts for collaborative ASW using AUVs. As part of this effort the design of autonomy algorithms (behaviours) which are adaptive on Doppler-sensitive sonar signals is being pursued. MOOS-IvP is currently used onboard two Ocean Explorer AUVs which each have horizontal line arrays and accompanying CW signal processing software capable of converting acoustic signals into time-bearing-Doppler contacts. These contacts are fused with FM contacts within NURC's DMHT tracker. The fused CW-FM tracks are acted on by the behaviours implemented within the MOOS-IvP software architecture. In this talk we explore the performance of a behaviour which seeks to maximize the future Doppler on contacts of interest. The collaborative use of this behaviour with a second vehicle performing traditional FM processing is also considered.
* MOOS-IvP
No Abstract Yet.
[[Talk.04-Redfield|Prev-Talk]] | [[Talk.04-Redfield|Next-Talk]]
* Multi-Vehicle Autonomy
* USVs
* Environmental Sampling
One of the greatest challenges of working in the underwater regime is the severe limitations of acoustic communications. This problem becomes even more evident in multi-vehicle autonomy, when vehicles must continually update each other with their state and intentions to achieve cooperative goals. In order to support tests of a multi-vehicle arbiter framework, an optimization scheme was created and implemented as a MOOS module to enable sufficient message passing between vehicles. Using this tool, vehicle state and destination, shared map updates, updated algorithm parameters, target information, and decision reconciliation can be effectively shared between vehicles using the published Compact Control Language (CCL) standard for acoustic messages.
Title: Unmanned Robot Message Optimization Method (URMOM)
Andrew Bouchard, NSWC PCD
One of the greatest challenges of working in the underwater regime is the
severe limitations of acoustic communications. This problem becomes even more evident in multi-vehicle autonomy, when vehicles must continually update each other with their state and intentions to achieve cooperative goals. In order to support tests of a multi-vehicle arbiter framework, an optimization scheme was created and implemented as a MOOS module to enable sufficient message passing between vehicles. Using this tool, vehicle state and destination, shared map updates, updated algorithm parameters, target information, and decision reconciliation can be effectively shared between vehicles using the published Compact Control Language (CCL) standard for acoustic messages.
26-Benjamin
Talk.26-Benjamin History
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* '''RobotX''': The 2014 Maritime RobotX Competition
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* '''RobotX''': The 2014 Maritime RobotX Competition (and our anticipated 2016 involvement)
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* '''Aquaticus''': A Human-Machine Competition in the Marine Ennvironment
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* '''Aquaticus''': A Human-Machine Competition in the Marine Environment
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* Payload Autonomy / Platform Interface
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* AUVs
* COLREGS
* COLREGS
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This talk provides a quick overview to newcomers of either the MOOS-DAWG workshop or the MOOS-IvP community. We are also users of our own software, and many of the recent and upcoming additions and improvements are driven by our research projects. In this talk we provide a simultaneous overview of these projects, highlighting the recent sofware additions, and why they were needed. We also describe additions and improvements expected in the next year or so driven by current projects.
to:
This talk provides a quick overview to newcomers of either the MOOS-DAWG workshop or the MOOS-IvP community. We are also users of our own software, and many of the recent and upcoming additions and improvements are driven by our research projects. In this talk we provide a simultaneous overview of these projects, highlighting the recent sofware additions, and why they were needed. We also describe additions and improvements expected in the next year or so driven by current projects. These projects include:
Changed lines 19-25 from:
* RobotX: The 2014 Maritime RobotX Competition
* Aquaticus: A Human-Machine Competition in the Marine Ennvironment
* COLREGS Autonomy: Protocol-based Collision Avoidance Algorithms for Following the Maritime Rules of the Road using Multi-Objective Optimization
* MIT 2.680: A New MIT Course on Marine Autonomy, Sensing and Communication
* Aquaticus: A Human-Machine Competition in the Marine Ennvironment
* COLREGS Autonomy: Protocol-based Collision Avoidance Algorithms for Following the Maritime Rules of the Road using Multi-Objective Optimization
* MIT 2.680: A New MIT Course on Marine Autonomy, Sensing and Communication
to:
* '''RobotX''': The 2014 Maritime RobotX Competition
* '''Aquaticus''': A Human-Machine Competition in the Marine Ennvironment
* '''COLREGS Autonomy''': Protocol-based Collision Avoidance Algorithms for Following the Maritime Rules of the Road using Multi-Objective Optimization
* '''MIT 2.680''': A New MIT Course on Marine Autonomy, Sensing and Communication
* '''Aquaticus''': A Human-Machine Competition in the Marine Ennvironment
* '''COLREGS Autonomy''': Protocol-based Collision Avoidance Algorithms for Following the Maritime Rules of the Road using Multi-Objective Optimization
* '''MIT 2.680''': A New MIT Course on Marine Autonomy, Sensing and Communication
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* RobotX: The 2014 Maritime RobotX Competition
* Aquaticus: A Human-Machine Competition in the Marine Ennvironment
* COLREGS Autonomy: Protocol-based Collision Avoidance Algorithms for Following the Maritime Rules of the Road using Multi-Objective Optimization
* MIT 2.680: A New MIT Course on Marine Autonomy, Sensing and Communication
* '''Hunter-Prey''': An In-water competition of Autonomous Search for Evasive Underwater Targets
* Aquaticus: A Human-Machine Competition in the Marine Ennvironment
* COLREGS Autonomy: Protocol-based Collision Avoidance Algorithms for Following the Maritime Rules of the Road using Multi-Objective Optimization
* MIT 2.680: A New MIT Course on Marine Autonomy, Sensing and Communication
* '''Hunter-Prey''': An In-water competition of Autonomous Search for Evasive Underwater Targets
Changed line 17 from:
This talk provides a quick overview to newcomers of either the MOOS-DAWG workshop or the MOOS-IvP on-line community. We are also users of our own software, and many of the recent and upcoming additions and improvements are driven by our research projects. In this talk we provide a simultaneous overview of these projects, highlighting the recent sofware additions, and why they were needed. We also describe additions and improvements expected in the next year or so driven by current projects.
to:
This talk provides a quick overview to newcomers of either the MOOS-DAWG workshop or the MOOS-IvP community. We are also users of our own software, and many of the recent and upcoming additions and improvements are driven by our research projects. In this talk we provide a simultaneous overview of these projects, highlighting the recent sofware additions, and why they were needed. We also describe additions and improvements expected in the next year or so driven by current projects.
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!! Talk-26: ''An Overview, Recent Events and Planned Additions''
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!! Talk-26: ''MOOS-IvP: An Overview, Recent Events and Planned Additions''
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!! Talk-25: ''The MIT/Olin RobotX Vehicle / The Kingfisher Payload Autonomy Computer''
!!!!%color=#449944% '''Alon Yaari, MIT'''
This talk will describe MITs entry to the 2014 RobotX Challenge and will also describe the PABLO, a waterproof computer designed to implement MOOS as a back-seat interface.
In October 2014,Office of Naval Research (ONR) and the Association of Unmanned Vehicles Systems International (AUVSI) conducted the first Maritime RobotX Challenge in Marina Bay, Singapore. Team MIT-Olin was one of 15 teams that competed, using a 16-foot WAM-V vessel provided by ONR. The team added propulsion, computing, and sensing hardware. The competition involved five tasks that required vehicles to travel between buoys, avoid obstacles, park and back out of a dock space, and localize an acoustic pinger. This talk describes the competition and how our software solutions were implemented in MOOS-IvP.
Payload Autonomy is a hardware and software solution for the front-seat/back-seat paradigm. The PABLO (Payload Autonomy Box) was developed at MIT to serve as a dedicated back-seat computing environment. The initial version of the box is designed to interface to the Clearpath Robotics Kingfisher M200 vehicle but the system can be customized to any front-seat with a known control interface. PABLO uses a Raspberry Pi and is low power, small, and features common waterproof interface connectors. PABLO boxes will be on-hand during the talk for those interested.
!!!!%color=#449944% '''
In October 2014,
Payload Autonomy is a hardware and software solution for the front-seat/back-seat paradigm
to:
!! Talk-26: ''An Overview, Recent Events and Planned Additions''
!!!!%color=#449944% '''Mike Benjamin, MIT'''
MOOS-IvP is an Open Source Autonomy project comprised of the MOOS middleware and the IvP Helm Autonomy architecture. Both architectures enable the incremental advancement of algorithms from diverse sources. Users can augment the openly distributed software with their own modules and are free to develop them as either open, proprietary or even classified additions.
This talk provides a quick overview to newcomers of either the MOOS-DAWG workshop or the MOOS-IvP on-line community. We are also users of our own software, and many of the recent and upcoming additions and improvements are driven by our research projects. In this talk we provide a simultaneous overview of these projects, highlighting the recent sofware additions, and why they were needed. We also describe additions and improvements expected in the next year or so driven by current projects.
!!!!%color=#449944% '''Mike Benjamin, MIT'''
MOOS-IvP is an Open Source Autonomy project comprised of the MOOS middleware and the IvP Helm Autonomy architecture. Both architectures enable the incremental advancement of algorithms from diverse sources. Users can augment the openly distributed software with their own modules and are free to develop them as either open, proprietary or even classified additions.
This talk provides a quick overview to newcomers of either the MOOS-DAWG workshop or the MOOS-IvP on-line community. We are also users of our own software, and many of the recent and upcoming additions and improvements are driven by our research projects. In this talk we provide a simultaneous overview of these projects, highlighting the recent sofware additions, and why they were needed. We also describe additions and improvements expected in the next year or so driven by current projects.
Changed lines 17-19 from:
In October 2014, the Office of Naval Research (ONR) and the Association of Unmanned Vehicles Systems International (AUVSI) conducted the first Maritime RobotX Challenge in Marina Bay, Singapore. Team MIT-Olin was one of 15 teams that competed, using a 16-foot WAM-V vessel provided by ONR. The team added propulsion, computing, and sensing hardware. The competition involved five tasks that required vehicles to travel between buoys, avoid obstacles, park and back out of a dock space, and localize an acoustic pinger. This talk describes the competition and how MOOS-IvP provided our software solutions.
Payload Autonomy is a hardware and software solution for the front-seat/back-seat paradigm. The PABLO (Payload Autonomy Box) was developed at MIT to serve as a dedicated back-seat computing environment. The initial version of the box is designed to interface to the Clearpath Robotics Kingfisher M200 vehicle but the system can be customized to any front-seat with a known control interface. PABLO uses a Raspberry Pi and is low power, small, and features common waterproof interface connectors.
Payload Autonomy is a hardware and software solution for the
to:
In October 2014, the Office of Naval Research (ONR) and the Association of Unmanned Vehicles Systems International (AUVSI) conducted the first Maritime RobotX Challenge in Marina Bay, Singapore. Team MIT-Olin was one of 15 teams that competed, using a 16-foot WAM-V vessel provided by ONR. The team added propulsion, computing, and sensing hardware. The competition involved five tasks that required vehicles to travel between buoys, avoid obstacles, park and back out of a dock space, and localize an acoustic pinger. This talk describes the competition and how our software solutions were implemented in MOOS-IvP.
Payload Autonomy is a hardware and software solution for the front-seat/back-seat paradigm. The PABLO (Payload Autonomy Box) was developed at MIT to serve as a dedicated back-seat computing environment. The initial version of the box is designed to interface to the Clearpath Robotics Kingfisher M200 vehicle but the system can be customized to any front-seat with a known control interface. PABLO uses a Raspberry Pi and is low power, small, and features common waterproof interface connectors. PABLO boxes will be on-hand during the talk for those interested.
Payload Autonomy is a hardware and software solution for the front-seat/back-seat paradigm. The PABLO (Payload Autonomy Box) was developed at MIT to serve as a dedicated back-seat computing environment. The initial version of the box is designed to interface to the Clearpath Robotics Kingfisher M200 vehicle but the system can be customized to any front-seat with a known control interface. PABLO uses a Raspberry Pi and is low power, small, and features common waterproof interface connectors. PABLO boxes will be on-hand during the talk for those interested.
Changed line 17 from:
In October 2014, the Office of Naval Research (ONR) and the Association of Unmanned Vehicles Systems International (AUVSI) conducted the first Maritime RobotX Challenge in Marina Bay, Singapore. Team MIT-Olin was one of 15 teams that competed, using a 16-foot WAM-V vessel provided by ONR. The team added propulsion, computing, and sending hardware. The competition involved five tasks that required vehicles to travel between buoys, avoid obstacles, park and back out of a dock space, and localize an acoustic pinger. This talk describes the competition and how MOOS-IvP provided our software solutions.
to:
In October 2014, the Office of Naval Research (ONR) and the Association of Unmanned Vehicles Systems International (AUVSI) conducted the first Maritime RobotX Challenge in Marina Bay, Singapore. Team MIT-Olin was one of 15 teams that competed, using a 16-foot WAM-V vessel provided by ONR. The team added propulsion, computing, and sensing hardware. The competition involved five tasks that required vehicles to travel between buoys, avoid obstacles, park and back out of a dock space, and localize an acoustic pinger. This talk describes the competition and how MOOS-IvP provided our software solutions.
Changed line 17 from:
In October 2014, the Office of Naval Research (ONR) and the Association of Unmanned Vehicles Systems International (AUVSI) conducted the first Maritime RobotX Challenge in Marina Bay, Singapore. Team MIT-Olin was one of 15 teams that competed, using a 16 WAM-V vessel provided by ONR. The team added propulsion, computing, and sending hardware. The competition involved five tasks that required vehicles to travel between buoys, avoid obstacles, park and back out of a dock space, and localize an acoustic pinger. This talk describes the competition and how MOOS-IvP provided our software solutions.
to:
In October 2014, the Office of Naval Research (ONR) and the Association of Unmanned Vehicles Systems International (AUVSI) conducted the first Maritime RobotX Challenge in Marina Bay, Singapore. Team MIT-Olin was one of 15 teams that competed, using a 16-foot WAM-V vessel provided by ONR. The team added propulsion, computing, and sending hardware. The competition involved five tasks that required vehicles to travel between buoys, avoid obstacles, park and back out of a dock space, and localize an acoustic pinger. This talk describes the competition and how MOOS-IvP provided our software solutions.
Added lines 15-19:
This talk will describe MITs entry to the 2014 RobotX Challenge and will also describe the PABLO, a waterproof computer designed to implement MOOS as a back-seat interface.
In October 2014, the Office of Naval Research (ONR) and the Association of Unmanned Vehicles Systems International (AUVSI) conducted the first Maritime RobotX Challenge in Marina Bay, Singapore. Team MIT-Olin was one of 15 teams that competed, using a 16 WAM-V vessel provided by ONR. The team added propulsion, computing, and sending hardware. The competition involved five tasks that required vehicles to travel between buoys, avoid obstacles, park and back out of a dock space, and localize an acoustic pinger. This talk describes the competition and how MOOS-IvP provided our software solutions.
Payload Autonomy is a hardware and software solution for the front-seat/back-seat paradigm. The PABLO (Payload Autonomy Box) was developed at MIT to serve as a dedicated back-seat computing environment. The initial version of the box is designed to interface to the Clearpath Robotics Kingfisher M200 vehicle but the system can be customized to any front-seat with a known control interface. PABLO uses a Raspberry Pi and is low power, small, and features common waterproof interface connectors.
In October 2014, the Office of Naval Research (ONR) and the Association of Unmanned Vehicles Systems International (AUVSI) conducted the first Maritime RobotX Challenge in Marina Bay, Singapore. Team MIT-Olin was one of 15 teams that competed, using a 16 WAM-V vessel provided by ONR. The team added propulsion, computing, and sending hardware. The competition involved five tasks that required vehicles to travel between buoys, avoid obstacles, park and back out of a dock space, and localize an acoustic pinger. This talk describes the competition and how MOOS-IvP provided our software solutions.
Payload Autonomy is a hardware and software solution for the front-seat/back-seat paradigm. The PABLO (Payload Autonomy Box) was developed at MIT to serve as a dedicated back-seat computing environment. The initial version of the box is designed to interface to the Clearpath Robotics Kingfisher M200 vehicle but the system can be customized to any front-seat with a known control interface. PABLO uses a Raspberry Pi and is low power, small, and features common waterproof interface connectors.
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* sNavigation/SLAM
* Sensors/Sonar
* Sensors/Sonar
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* Payload Autonomy / Platform Interface
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!! Talk-12: ''Data association towards persistent SLAM with underwater sideScan sonar implemented in MOOS-IvP''
!!!!%color=#449944% '''Mae L. Seto, Defence Research and Development Canada'''
!!!!%color=#449944% '''Colin MacKenzie'''
!!!!%color=#449944% '''Timothy Pohajdak'''
!!!!%color=#449944% '''John J. Leonard, MIT'''
While critical to the simultaneous localization and mapping (SLAM) process data association can be unreliable and especially so in the noisy, dynamic underwater environment. This presentation discusses a data association algorithm to enhance underwater SLAM on autonomous underwater vehicles (AUV) with side-scan sonars through jointly associating the estimated relative position of a landmark to the AUV with the local seafloor elevation gradients surrounding the landmark. The local elevation gradients are extracted from the same side-scan sonar images that the landmarks are detected in. As environmental features, the gradients are less susceptible to gross changes due to the underwater environment compared to a small (~ few meters) landmark.
This scheme yields realistic correct associations when implemented in MOOS and validated in a hardware-in-the-loop AUV simulator using sidescan sonar data from earlier trials. The data association algorithm is now integrated into the iSAM (incremental smoothing and mapping) dynamic pose graph formulation and undergoing in-water validation as an on-line MOOS capability.
!!!!%color=#449944% '''
!!!!%color=#449944% '''Colin MacKenzie'''
!!!!%color=#449944% '''Timothy Pohajdak'''
!!!!%color=#449944% '''John J. Leonard, MIT'''
While critical to the simultaneous localization and mapping (SLAM) process data association can be unreliable and especially so in the noisy, dynamic underwater environment. This presentation discusses a data association algorithm to enhance underwater SLAM on autonomous underwater vehicles (AUV) with side-scan sonars through jointly associating the estimated relative position of a landmark to the AUV with the local seafloor elevation gradients surrounding the landmark. The local elevation gradients are extracted from the same side-scan sonar images that the landmarks are detected in. As environmental features, the gradients are less susceptible to gross changes due to the underwater environment compared to a small (~ few meters) landmark.
This scheme yields realistic correct associations when implemented in MOOS and validated in a hardware-in-the-loop AUV simulator using sidescan sonar data from earlier trials. The data association algorithm is now integrated into the iSAM (incremental smoothing and mapping) dynamic pose graph formulation and undergoing in-water validation as an on-line MOOS capability.
to:
!! Talk-25: ''The MIT/Olin RobotX Vehicle / The Kingfisher Payload Autonomy Computer''
!!!!%color=#449944% '''Alon Yaari, MIT'''
!!!!%color=#449944% '''Alon Yaari, MIT'''
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* AUVs
* Navigation/SLAM
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* ASVs
* sNavigation/SLAM
* sNavigation/SLAM
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* Payload Autonomy Interface
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* UUVs
* Navigation/SLAM
* Sensors/Sonar
* Navigation/SLAM
* Sensors/Sonar
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!! Talk-11: ''Implementing on-line AUV SLAM using side-scan sonar and automated target detection with MOOS- IvP''
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!! Talk-12: ''Data association towards persistent SLAM with underwater sideScan sonar implemented in MOOS-IvP''
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!!!!%color=#449944% '''Colin MacKenzie'''
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The payload autonomy was implemented in MOOS
Three sets of in-water trials, separated by weeks, were performed to collect the change detection data sets. A year later a newer data set was collected over the same area with the AUV fusing a high calibre INS into the dead-reckoned navigation solution. The presentation discusses the use of the MOOS framework for implementing in-water SLAM using the DRDC ATD tools and iSAM on an embedded processor. As a measure of localization accuracy, the internal dead-reckoned AUV position estimate (with and without INS) just before surfacing is compared with the GPS fix on the surface and with the SLAM position estimate
to:
While critical to the simultaneous localization and mapping (SLAM) process data association can be unreliable and especially so in the noisy, dynamic underwater environment. This presentation discusses a data association algorithm to enhance underwater SLAM on autonomous underwater vehicles (AUV) with side-scan sonars through jointly associating the estimated relative position of a landmark to the AUV with the local seafloor elevation gradients surrounding the landmark. The local elevation gradients are extracted from the same side-scan sonar images that the landmarks are detected in. As environmental features, the gradients are less susceptible to gross changes due to the underwater environment compared to a small (~ few meters) landmark.
This scheme yields realistic correct associations when implemented in MOOS and validated in a hardware-in-the-loop AUV simulator using sidescan sonar data from earlier trials. The data association algorithm is now integrated into the iSAM (incremental smoothing and mapping) dynamic pose graph formulation and undergoing in-water validation as an on-line MOOS capability.
This scheme yields realistic correct associations when implemented in MOOS and validated in a hardware-in-the-loop AUV simulator using sidescan sonar data from earlier trials. The data association algorithm is now integrated into the iSAM (incremental smoothing and mapping) dynamic pose graph formulation and undergoing in-water validation as an on-line MOOS capability.
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!! Talk-10: ''A Flexible, Low-Cost MOOS-IvP Based Platform for Marine Autonomy Research''
!!!!%color=#449944% '''James Keane, Australian Maritime College, University of Tasmania, Royal Australian Navy'''
!!!!%color=#449944% '''Alexander Forrest, Australian Maritime College, University of Tasmania, Tahoe Environmental Research Center, University of California.'''
!!!!%color=#449944% ''' Hordur Johannsson, Teledyne Gavia'''
!!!!%color=#449944% '''Jonathan Duffy, Australian Maritime College, University of Tasmania'''
!!!!%color=#449944% '''David Battle, Australian Defence Science Technology Organisation.'''
Homing behavior for Autonomous Underwater Vehicles (AUVs) is vital for autonomous multi-platform docking and also indispensable for recovery of vehicles deployed beneath ice shelves. Mission Oriented Operating Suite(MOOS) is currently being implemented as a backseat driver on a Teledyne Gavia AUV to enhance the AUV with adaptive manoeuvring capabilities; in turn enabling homing to a single LinkQuest 1500 Long BaseLine (LBL) acoustic transponder.
A homing application (pHomeToLBL) has been developed in the MOOS simulator which uses LBL Range Reports to estimate transponder location, and hence update vehicle return waypoints through the MOOS database (MOOSDB). The trilateration-based homing algorithm was applied to raw mission data (from Antarctic sub-ice deployments) where two transponder were present and consistently located deployed LBLs with an accuracy linearly proportional to the uncertainty of the LBL Range Reports. Simultaneously, collaboration with Teledyne Gavia software engineers is leading to the compilation of MOOS-IvP (with the pHomeToLBL application) on the Gavia main vehicle computer.
Field trials to demonstrate MOOS-IvP-GAVIA and homing are scheduled for June 2015. Field trials will be an industry-firstof deploying a user-developed application on MOOS-IvP-GAVIA, and furthermore, of having a Gavia enhanced with adaptive manoeuvring capabilities for homing. Ultimately, the MOOS-IvP-GAVIA backseat driver will enable this AUV to home to an acoustic transponder (stationary or underway) but the trilateration algorithm could be used on any AUV with MOOS as a backseat driver in preparation for autonomous docking.
!!!!%color=#449944% '''
!!!!%color=#449944% '''
!!!!%color=#449944% ''' Hordur Johannsson, Teledyne Gavia'''
!!!!%color=#449944% '''Jonathan Duffy, Australian Maritime College, University of Tasmania'''
!!!!%color=#449944% '''David Battle, Australian Defence Science Technology Organisation.'''
Homing behavior for Autonomous Underwater Vehicles (AUVs) is vital for autonomous multi-platform docking and also indispensable for recovery of vehicles deployed beneath ice shelves. Mission Oriented Operating Suite
A homing application (pHomeToLBL) has been developed in the MOOS simulator which uses LBL Range Reports to estimate transponder location, and hence update vehicle return waypoints through the MOOS database (MOOSDB). The trilateration-based homing algorithm was applied to raw mission data (from Antarctic sub-ice deployments) where two transponder were present and consistently located deployed LBLs with an accuracy linearly proportional to the uncertainty of
Field trials to demonstrate MOOS-IvP-GAVIA and homing are scheduled for June 2015. Field trials will be an industry-first
to:
!! Talk-11: ''Implementing on-line AUV SLAM using side-scan sonar and automated target detection with MOOS- IvP''
!!!!%color=#449944% '''Mae L. Seto, Defence Research and Development Canada'''
!!!!%color=#449944% '''Timothy Pohajdak'''
!!!!%color=#449944% '''Colin MacKenzie'''
!!!!%color=#449944% '''John J. Leonard, MIT'''
Simultaneous localization and mapping (SLAM) using side-scan sonar on-board autonomous underwater vehicles (AUV) for mapping mine-like objects (or targets) was implemented and tested in- water. The work is motivated by persistent SLAM for change detection over longer periods.
The payload autonomy was implemented in MOOS-IvP on-board the backseat processor of the IVER AUV. iSAM (incremental smoothing and mapping) was the SLAM engine used for the core state estimation. The DRDC automated target detection (ATD) tools were used to perform the on-line target detection and identification as well as provide an estimate for the target location. The data association used was joint compatibility branch and bound. These tools and algorithms were integrated and/or developed with a hardware-in-the loop simulator for the Iver AUV. Newly developed MOOS modules are discussed.
Three sets of in-water trials, separated by weeks, were performed to collect the change detection data sets. A year later a newer data set was collected over the same area with the AUV fusing a high calibre INS into the dead-reckoned navigation solution. The presentation discusses the use of the MOOS framework for implementing in-water SLAM using the DRDC ATD tools and iSAM on an embedded processor. As a measure of localization accuracy, the internal dead-reckoned AUV position estimate (with and without INS) just before surfacing is compared with the GPS fix on the surface and with the SLAM position estimate.
!!!!%color=#449944% '''Mae L. Seto, Defence Research and Development Canada'''
!!!!%color=#449944% '''Timothy Pohajdak'''
!!!!%color=#449944% '''Colin MacKenzie'''
!!!!%color=#449944% '''John J. Leonard, MIT'''
Simultaneous localization and mapping (SLAM) using side-scan sonar on-board autonomous underwater vehicles (AUV) for mapping mine-like objects (or targets) was implemented and tested in- water. The work is motivated by persistent SLAM for change detection over longer periods.
The payload autonomy was implemented in MOOS-IvP on-board the backseat processor of the IVER AUV. iSAM (incremental smoothing and mapping) was the SLAM engine used for the core state estimation. The DRDC automated target detection (ATD) tools were used to perform the on-line target detection and identification as well as provide an estimate for the target location. The data association used was joint compatibility branch and bound. These tools and algorithms were integrated and/or developed with a hardware-in-the loop simulator for the Iver AUV. Newly developed MOOS modules are discussed.
Three sets of in-water trials, separated by weeks, were performed to collect the change detection data sets. A year later a newer data set was collected over the same area with the AUV fusing a high calibre INS into the dead-reckoned navigation solution. The presentation discusses the use of the MOOS framework for implementing in-water SLAM using the DRDC ATD tools and iSAM on an embedded processor. As a measure of localization accuracy, the internal dead-reckoned AUV position estimate (with and without INS) just before surfacing is compared with the GPS fix on the surface and with the SLAM position estimate.
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!! Talk-08: ''Developing an autonomous low cost boat using MOOS-IvP''
!!!!%color=#449944% '''David Issa Mattos, Douglas S. dos Santos, Cairo L. Nascimento Jr., Instituto Tecnologico de Aeronutica, Brazil'''
The development of autonomous vehicles provides the opportunity to create new combinations of hardware components and their software interfaces with an autonomous software library. The platform present in this work is a catamaran boat driven by two direct current motors as the propulsion system. It is embedded with an Arduino, a low cost IMU (Inertial Measurement Unit), a GPS receiver and a wireless serial communication system. The boat communicates with the ground control station (GCS) sending telemetry data and receiving navigation commands for the propulsion motors. The GCS uses the MOOS-IvP software to implement the navigation decision and GPS/IMU fusion algorithms. A boat model is presented and used in a simulation environment module. Results from simulation and real-world testing will be presented and discussed.
!!!!%color=#449944% '''
to:
!! Talk-10: ''A Flexible, Low-Cost MOOS-IvP Based Platform for Marine Autonomy Research''
!!!!%color=#449944% '''James Keane, Australian Maritime College, University of Tasmania, Royal Australian Navy'''
!!!!%color=#449944% '''Alexander Forrest, Australian Maritime College, University of Tasmania, Tahoe Environmental Research Center, University of California.'''
!!!!%color=#449944% ''' Hordur Johannsson, Teledyne Gavia'''
!!!!%color=#449944% '''Jonathan Duffy, Australian Maritime College, University of Tasmania'''
!!!!%color=#449944% '''David Battle, Australian Defence Science Technology Organisation.'''
Homing behavior for Autonomous Underwater Vehicles (AUVs) is vital for autonomous multi-platform docking and also indispensable for recovery of vehicles deployed beneath ice shelves. Mission Oriented Operating Suite (MOOS) is currently being implemented as a backseat driver on a Teledyne Gavia AUV to enhance the AUV with adaptive manoeuvring capabilities; in turn enabling homing to a single LinkQuest 1500 Long BaseLine (LBL) acoustic transponder.
A homing application (pHomeToLBL) has been developed in the MOOS simulator which uses LBL Range Reports to estimate transponder location, and hence update vehicle return waypoints through the MOOS database (MOOSDB). The trilateration-based homing algorithm was applied to raw mission data (from Antarctic sub-ice deployments) where two transponder were present and consistently located deployed LBLs with an accuracy linearly proportional to the uncertainty of the LBL Range Reports. Simultaneously, collaboration with Teledyne Gavia software engineers is leading to the compilation of MOOS-IvP (with the pHomeToLBL application) on the Gavia main vehicle computer.
Field trials to demonstrate MOOS-IvP-GAVIA and homing are scheduled for June 2015. Field trials will be an industry-first of deploying a user-developed application on MOOS-IvP-GAVIA, and furthermore, of having a Gavia enhanced with adaptive manoeuvring capabilities for homing. Ultimately, the MOOS-IvP-GAVIA backseat driver will enable this AUV to home to an acoustic transponder (stationary or underway) but the trilateration algorithm could be used on any AUV with MOOS as a backseat driver in preparation for autonomous docking.
!!!!%color=#449944% '''James Keane, Australian Maritime College, University of Tasmania, Royal Australian Navy'''
!!!!%color=#449944% '''Alexander Forrest, Australian Maritime College, University of Tasmania, Tahoe Environmental Research Center, University of California.'''
!!!!%color=#449944% ''' Hordur Johannsson, Teledyne Gavia'''
!!!!%color=#449944% '''Jonathan Duffy, Australian Maritime College, University of Tasmania'''
!!!!%color=#449944% '''David Battle, Australian Defence Science Technology Organisation.'''
Homing behavior for Autonomous Underwater Vehicles (AUVs) is vital for autonomous multi-platform docking and also indispensable for recovery of vehicles deployed beneath ice shelves. Mission Oriented Operating Suite (MOOS) is currently being implemented as a backseat driver on a Teledyne Gavia AUV to enhance the AUV with adaptive manoeuvring capabilities; in turn enabling homing to a single LinkQuest 1500 Long BaseLine (LBL) acoustic transponder.
A homing application (pHomeToLBL) has been developed in the MOOS simulator which uses LBL Range Reports to estimate transponder location, and hence update vehicle return waypoints through the MOOS database (MOOSDB). The trilateration-based homing algorithm was applied to raw mission data (from Antarctic sub-ice deployments) where two transponder were present and consistently located deployed LBLs with an accuracy linearly proportional to the uncertainty of the LBL Range Reports. Simultaneously, collaboration with Teledyne Gavia software engineers is leading to the compilation of MOOS-IvP (with the pHomeToLBL application) on the Gavia main vehicle computer.
Field trials to demonstrate MOOS-IvP-GAVIA and homing are scheduled for June 2015. Field trials will be an industry-first of deploying a user-developed application on MOOS-IvP-GAVIA, and furthermore, of having a Gavia enhanced with adaptive manoeuvring capabilities for homing. Ultimately, the MOOS-IvP-GAVIA backseat driver will enable this AUV to home to an acoustic transponder (stationary or underway) but the trilateration algorithm could be used on any AUV with MOOS as a backseat driver in preparation for autonomous docking.
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!!!! %color=#7777BB% [[Talk.06-Sideleau | Prev-Talk]]%% | \
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!!!! %color=#7777BB% [[Talk.07-Kutzke | Prev-Talk]]%% | \
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!! Talk-07: ''Developing MOOS-IvP interfaces to OPL/CPLEX Optimization Software and a Novel Mission Visualizer''
!!!!%color=#449944% '''Demetrious Kutzke, Marquette University'''
!!!!%color=#449944% '''Mikhail Yakhnis, Cornell University'''
!!!!%color=#449944% '''Jim Perkins, Matthew Bays, Naval Surface Warfare Center, Panama City Division'''
!!!!%color=#449944% '''
!!!!%color=#449944% '''Mikhail Yakhnis, Cornell University'''
!!!!%color=#449944% '''Jim Perkins, Matthew Bays, Naval Surface Warfare Center
to:
!! Talk-08: ''Developing an autonomous low cost boat using MOOS-IvP''
!!!!%color=#449944% '''David Issa Mattos, Douglas S. dos Santos, Cairo L. Nascimento Jr., Instituto Tecnologico de Aeronutica, Brazil'''
The development of autonomous vehicles provides the opportunity to create new combinations of hardware components and their software interfaces with an autonomous software library. The platform present in this work is a catamaran boat driven by two direct current motors as the propulsion system. It is embedded with an Arduino, a low cost IMU (Inertial Measurement Unit), a GPS receiver and a wireless serial communication system. The boat communicates with the ground control station (GCS) sending telemetry data and receiving navigation commands for the propulsion motors. The GCS uses the MOOS-IvP software to implement the navigation decision and GPS/IMU fusion algorithms. A boat model is presented and used in a simulation environment module. Results from simulation and real-world testing will be presented and discussed.
!!!!%color=#449944% '''David Issa Mattos, Douglas S. dos Santos, Cairo L. Nascimento Jr., Instituto Tecnologico de Aeronutica, Brazil'''
The development of autonomous vehicles provides the opportunity to create new combinations of hardware components and their software interfaces with an autonomous software library. The platform present in this work is a catamaran boat driven by two direct current motors as the propulsion system. It is embedded with an Arduino, a low cost IMU (Inertial Measurement Unit), a GPS receiver and a wireless serial communication system. The boat communicates with the ground control station (GCS) sending telemetry data and receiving navigation commands for the propulsion motors. The GCS uses the MOOS-IvP software to implement the navigation decision and GPS/IMU fusion algorithms. A boat model is presented and used in a simulation environment module. Results from simulation and real-world testing will be presented and discussed.
Deleted line 16:
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!! Talk-03: ''Cross-Domain UxV Collaboration Scenario Development with MOOS-IvP''
!!!!%color=#449944% '''Lonnie Parker, Christopher Gagner, Scott Sideleau, Michael Incze, NUWC-DIVNPT'''
The future of cross-domain autonomy (any combination of air, land, and sea platforms) will reside in providing value to time-sensitive applications. Those scenarios in which important information is collected by one platform, but requires exfiltration by another has uses in search-rescue missions, port-monitoring, and oceanographic surveys, to name a few. Any behaviors executed must be confined to achievable goals while also demonstrating flexibility to account for unplanned changes in situ. This work reports on the design and implementation of a multi-vehicle, cross-domain AUV scenario responsible for executing a combination of waypoint-based and advanced autonomy behaviors in response to direct commands from an unmanned aerial vehicle, via AUV gateway. The behaviors have been abstracted as contour-following and multi-scale bathymetry surveys, incorporating conditional pauses based on the state of shipping traffic detected by Automatic Identification System. The simulation created was designed to be vehicle agnostic to make allowances for unexpected system state changes.
!!!!%color=#449944% '''
to:
!! Talk-07: ''Developing MOOS-IvP interfaces to OPL/CPLEX Optimization Software and a Novel Mission Visualizer''
!!!!%color=#449944% '''Demetrious Kutzke, Marquette University'''
!!!!%color=#449944% '''Mikhail Yakhnis, Cornell University'''
!!!!%color=#449944% '''Jim Perkins, Matthew Bays, Naval Surface Warfare Center, Panama City Division'''
As maritime autonomy is introduced to perform increasingly complex missions, there is a prevailing need to autonomously determine complex event sequences and task schedules, as well as visualizing those vehicle actions before a mission. The Naval Surface Warfare Center, Panama City Division (NSWC PCD) has developed interfaces between MOOS-IvP to aid with both areas. We present a software framework to interface the commercial off-the-shelf optimization software OPL/CPLEX with MOOS-IvP. IBM's OPL/CPLEX is one of the world’s fastest optimization software packages for solving numerical optimization problems such as mixed-integer linear programming, quadratic programming, and scheduling. The OPL-MOOS interface will allow unmanned systems controlled by the MOOS-IvP autonomy framework to utilize numerical optimization routines found in OPL/CPLEX for autonomous plan optimization and scheduling. For mission visualization, we present an overview of a novel three-dimensional mission visualization software package developed by NSWC PCD, and its interface with MOOS-IvP using Google protocol buffers. We believe the two interfaces and software packages allow for extending the possible mission profiles and tasking with which MOOS-IvP can be used.
!!!!%color=#449944% '''Demetrious Kutzke, Marquette University'''
!!!!%color=#449944% '''Mikhail Yakhnis, Cornell University'''
!!!!%color=#449944% '''Jim Perkins, Matthew Bays, Naval Surface Warfare Center, Panama City Division'''
As maritime autonomy is introduced to perform increasingly complex missions, there is a prevailing need to autonomously determine complex event sequences and task schedules, as well as visualizing those vehicle actions before a mission. The Naval Surface Warfare Center, Panama City Division (NSWC PCD) has developed interfaces between MOOS-IvP to aid with both areas. We present a software framework to interface the commercial off-the-shelf optimization software OPL/CPLEX with MOOS-IvP. IBM's OPL/CPLEX is one of the world’s fastest optimization software packages for solving numerical optimization problems such as mixed-integer linear programming, quadratic programming, and scheduling. The OPL-MOOS interface will allow unmanned systems controlled by the MOOS-IvP autonomy framework to utilize numerical optimization routines found in OPL/CPLEX for autonomous plan optimization and scheduling. For mission visualization, we present an overview of a novel three-dimensional mission visualization software package developed by NSWC PCD, and its interface with MOOS-IvP using Google protocol buffers. We believe the two interfaces and software packages allow for extending the possible mission profiles and tasking with which MOOS-IvP can be used.
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Since changes in the ocean floor are limited over reasonable time scales and continually laying new realistic synthetic mine fields is difficult groups are forced to take their vehicles further and further on field trips at great expense; both financially and in lost development time
Our simulator integrates with a MOOS simulation enabling complete missions to be run including all autonomy systems, acoustic communication and vehicle dynamics and control. The MOOS simulator provides regular updates on the vehicles real world position as well as its internal position estimate, which our simulator uses in combination with a high fidelity 3D mesh of the environment to provide simulated side scan sonar output, including estimated position information for each pulse. The side scan simulator itself uses the NVIDIA OptiX ray tracing framework to perform the core of the simulation on any available CUDA enabled compute card(s). In this presentation we present the simulator as applied particularly to a REMUS 100 vehicle, as that is the primary vehicle in use by DSTO for side scan based MCM.
to:
The future of cross-domain autonomy (any combination of air, land, and sea platforms) will reside in providing value to time-sensitive applications. Those scenarios in which important information is collected by one platform, but requires exfiltration by another has uses in search-rescue missions, port-monitoring, and oceanographic surveys, to name a few. Any behaviors executed must be confined to achievable goals while also demonstrating flexibility to account for unplanned changes in situ. This work reports on the design and implementation of a multi-vehicle, cross-domain AUV scenario responsible for executing a combination of waypoint-based and advanced autonomy behaviors in response to direct commands from an unmanned aerial vehicle, via AUV gateway. The behaviors have been abstracted as contour-following and multi-scale bathymetry surveys, incorporating conditional pauses based on the state of shipping traffic detected by Automatic Identification System. The simulation created was designed to be vehicle agnostic to make allowances for unexpected system state changes.
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!! Talk-02: ''Real-time Simulation of Side Scan Sonar within the MOOS framework using NVIDIA OptiX''
!!!!%color=#449944% '''Trevor G. Anderson, Australian Centre for Field Robotics, The University of Sydney'''
!!!!%color=#449944% '''David G. Johnson, Australian Centre for Field Robotics, The University of Sydney'''
!!!!%color=#449944% '''David Battle, Defence Science and Technology Organisation'''
!!!!%color=#449944% '''
!!!!%color=#449944% '''David G. Johnson, Australian Centre for Field Robotics, The University of Sydney'''
!!!!%color=#449944% '''David Battle, Defence Science and Technology Organisation
to:
!! Talk-03: ''Cross-Domain UxV Collaboration Scenario Development with MOOS-IvP''
!!!!%color=#449944% '''Lonnie Parker, Christopher Gagner, Scott Sideleau, Michael Incze, NUWC-DIVNPT'''
!!!!%color=#449944% '''Lonnie Parker, Christopher Gagner, Scott Sideleau, Michael Incze, NUWC-DIVNPT'''
Changed lines 15-17 from:
!!!!%color=#449944% '''David G. Johnson, Australian Centre for Field Robotics, The University of Sydney''
David Battle, Defence Science and Technology Organisation'''
to:
!!!!%color=#449944% '''David G. Johnson, Australian Centre for Field Robotics, The University of Sydney'''
!!!!%color=#449944% '''David Battle, Defence Science and Technology Organisation'''
!!!!%color=#449944% '''David Battle, Defence Science and Technology Organisation'''
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!!!!%color=#449944% '''David G. Johnson, Australian Centre for Field Robotics, The University of Sydney''
!!!!%color=#449944% ''' David Battle, Defence Science and Technology Organisation'''
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!! Talk-01: ''iFrontSeat: A New Approach for Writing Extensible MOOS-IvP Frontseat-Backseat Payload Interface Drivers''
!!!!%color=#449944% '''Toby Schneider, Massachusetts Institute of Technology (MIT)'''
Broadly, our goal at LAMSS is to develop a autonomy,sensing, and communications infrastructure that can operate on a heterogeneous collection of vehicles by splitting the system into two components: the "frontseat" and "backseat" computing systems. The "frontseat" is provided by the vehicle manufacturer and is typically proprietary. It is responsible for low level control of the vehicle. The "backseat" runs the high level autonomy (typically the IvP Helm), sensing, and communications (typically Goby) components.
Not surprisingly, a piece of software is required to interface between the "frontseat" and the "backseat". Historically, a new interface has been written for each vehicle that was to be used with MOOS-IvP (For example,applications iHuxley, iRecon, iOceanServerComms, ...). This led to a proliferation of approaches for handling the state transitions and control. In some cases, misunderstandings involving various aspects of MOOS-IvP have led to vehicle runaways. Furthermore, as MOOS-IvP becomes even more widely adopted and the number of manufacturers of robotic assets increases, it seems sensible to minimize the duplication of effort involved in writing interfaces.
iFrontSeat aims to address these problems by providing a single open source implementation of the connection to MOOS-IvP (the "backseat") while providing a structured well-defined extensible interface for
writing different "frontseat" drivers. Currently, a driver has been developed and tested for the Bluefin Robotics family of AUVs. This talk will discuss the design of iFrontSeat with a focus on how to expand its use to a wide variety of.
[[Path:/moos-dawg13/docs/01-schneider_brief.pdf | %color=#ff7f00% DOWNLOAD]]
!!!!%color=#449944% '''
Broadly
Not surprisingly, a piece of software is required to interface between the "frontseat" and the "backseat". Historically, a new interface has been written for each vehicle that was to be used with MOOS-IvP (For example,
iFrontSeat aims to address these problems by providing a single open source implementation of the connection to MOOS-IvP (the "backseat") while providing a structured well-defined extensible interface for
writing different "frontseat" drivers. Currently, a driver has been developed and tested for the Bluefin Robotics family of AUVs. This talk will discuss the design of iFrontSeat with a focus on how to expand its use to a wide variety of
[[Path:/moos-dawg13/docs/01-schneider_brief.pdf | %color=#ff7f00% DOWNLOAD]]
to:
!! Talk-02: ''Real-time Simulation of Side Scan Sonar within the MOOS framework using NVIDIA OptiX''
!!!!%color=#449944% '''Trevor G. Anderson, Australian Centre for Field Robotics, The University of Sydney'''
Autonomous underwater vehicles (AUVs) are regularly used as platforms for side scan sonar systems. One particularly common use is in the detection, identification and classification of underwater mines; known as mine countermeasures (MCM). Traditionally the development of processing algorithms for this task is performed iteratively; that is, a vehicle is taken out to an example mine field, either real or populated with training mines, where a selection of scans are collected. The vehicle simply records the collected data including navigational information which is then processed back at the “office” to train and improve the algorithms. When as much as possible has been gained from the latest set of data another trial is run, hopefully under different conditions so as to be useful in training.
Since changes in the ocean floor are limited over reasonable time scales and continually laying new realistic synthetic mine fields is difficult groups are forced to take their vehicles further and further on field trips at great expense; both financially and in lost development time. In collaboration with the Australian Defence Science and Technology Organisation (DSTO) we have developed a high fidelity, highly configurable real time side scan sonar simulator such that virtual trials in variable mine fields can facilitate rapid iterative development.
Our simulator integrates with a MOOS simulation enabling complete missions to be run including all autonomy systems, acoustic communication and vehicle dynamics and control. The MOOS simulator provides regular updates on the vehicles real world position as well as its internal position estimate, which our simulator uses in combination with a high fidelity 3D mesh of the environment to provide simulated side scan sonar output, including estimated position information for each pulse. The side scan simulator itself uses the NVIDIA OptiX ray tracing framework to perform the core of the simulation on any available CUDA enabled compute card(s). In this presentation we present the simulator as applied particularly to a REMUS 100 vehicle, as that is the primary vehicle in use by DSTO for side scan based MCM.
!!!!%color=#449944% '''Trevor G. Anderson, Australian Centre for Field Robotics, The University of Sydney'''
Autonomous underwater vehicles (AUVs) are regularly used as platforms for side scan sonar systems. One particularly common use is in the detection, identification and classification of underwater mines; known as mine countermeasures (MCM). Traditionally the development of processing algorithms for this task is performed iteratively; that is, a vehicle is taken out to an example mine field, either real or populated with training mines, where a selection of scans are collected. The vehicle simply records the collected data including navigational information which is then processed back at the “office” to train and improve the algorithms. When as much as possible has been gained from the latest set of data another trial is run, hopefully under different conditions so as to be useful in training.
Since changes in the ocean floor are limited over reasonable time scales and continually laying new realistic synthetic mine fields is difficult groups are forced to take their vehicles further and further on field trips at great expense; both financially and in lost development time. In collaboration with the Australian Defence Science and Technology Organisation (DSTO) we have developed a high fidelity, highly configurable real time side scan sonar simulator such that virtual trials in variable mine fields can facilitate rapid iterative development.
Our simulator integrates with a MOOS simulation enabling complete missions to be run including all autonomy systems, acoustic communication and vehicle dynamics and control. The MOOS simulator provides regular updates on the vehicles real world position as well as its internal position estimate, which our simulator uses in combination with a high fidelity 3D mesh of the environment to provide simulated side scan sonar output, including estimated position information for each pulse. The side scan simulator itself uses the NVIDIA OptiX ray tracing framework to perform the core of the simulation on any available CUDA enabled compute card(s). In this presentation we present the simulator as applied particularly to a REMUS 100 vehicle, as that is the primary vehicle in use by DSTO for side scan based MCM.
Changed lines 25-26 from:
* Bluefin Robotics
to:
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[[Path:/moos-dawg13/docs/01-schneider_brief.pdf | %color=#ff7f00% DOWNLOAD]]
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writing different "frontseat" drivers. Currently, a driver has been developed and tested for the Bluefin Robotics family of AUVs. This talk with discuss the design of iFrontSeat with a focus on how to expand its use to a wide variety of vehicles.
to:
writing different "frontseat" drivers. Currently, a driver has been developed and tested for the Bluefin Robotics family of AUVs. This talk will discuss the design of iFrontSeat with a focus on how to expand its use to a wide variety of vehicles.
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!!!! %color=#7777BB% [[Talk.01-LePage | Prev-Talk]]%% | \
%color=#7777BB%[[Talk.02-Schmidt | Next-Talk]]%% | \
%color=#7777BB%[[Talk.02-
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!!!! %color=#7777BB% [[Talk.01-Schneider | Prev-Talk]]%% | \
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* Anti-Submarine Warfare
*Autonomy / Collaborative Autonomy
* The Ocean Explorer UUV
* Unmanned Underwater Vehicles (UUVs) / Autonomous Underwater Vehicles (AUVs)
* MOOS-IvP
* IvP Helm Behavior Development
*
* The Ocean Explorer UUV
* Unmanned Underwater Vehicles (UUVs) / Autonomous Underwater Vehicles (AUVs)
* MOOS-IvP
* IvP Helm Behavior Development
to:
* Payload Autonomy Interface
* Bluefin Robotics
* Bluefin Robotics
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!! Talk-01: ''Behaviour Development for Anti-Submarine Warfare: The Design of a MOOS-IvP Behavior Based on Maximizing the Doppler of Autonomous Assets Operating Within a Bistatic Sonar System''
!!!!%color=#449944% '''Kevin LePage, NATO Undersea Research Centre (NURC)'''
Broadly, our goal at LAMSS is to develop a autonomy,sensing, and communications infrastructure that can operate on a heterogeneous collection of vehicles by splitting the system into two components: the "frontseat" and "backseat" computing systems. The "frontseat" is provided by the vehicle manufacturer and is typically proprietary. It is responsible for low level control of the vehicle. The "backseat" runs the high level autonomy (typically the IvP Helm), sensing, and communications (typically Goby) components.
!!!!%color=#449944% '''
to:
!! Talk-01: ''iFrontSeat: A New Approach for Writing Extensible MOOS-IvP Frontseat-Backseat Payload Interface Drivers''
!!!!%color=#449944% '''Toby Schneider, Massachusetts Institute of Technology (MIT)'''
Broadly, our goal at LAMSS is to develop a autonomy,sensing, and communications infrastructure that can operate on a heterogeneous collection of vehicles by splitting the system into two components: the "frontseat" and "backseat" computing systems. The "frontseat" is provided by the vehicle manufacturer and is typically proprietary. It is responsible for low level control of the vehicle. The "backseat" runs the high level autonomy (typically the IvP Helm), sensing, and communications (typically Goby) components.
!!!!%color=#449944% '''Toby Schneider, Massachusetts Institute of Technology (MIT)'''
Broadly, our goal at LAMSS is to develop a autonomy,sensing, and communications infrastructure that can operate on a heterogeneous collection of vehicles by splitting the system into two components: the "frontseat" and "backseat" computing systems. The "frontseat" is provided by the vehicle manufacturer and is typically proprietary. It is responsible for low level control of the vehicle. The "backseat" runs the high level autonomy (typically the IvP Helm), sensing, and communications (typically Goby) components.
Changed lines 15-19 from:
to:
Broadly, our goal at LAMSS is to develop a autonomy,sensing, and communications infrastructure that can operate on a heterogeneous collection of vehicles by splitting the system into two components: the "frontseat" and "backseat" computing systems. The "frontseat" is provided by the vehicle manufacturer and is typically proprietary. It is responsible for low level control of the vehicle. The "backseat" runs the high level autonomy (typically the IvP Helm), sensing, and communications (typically Goby) components.
Not surprisingly, a piece of software is required to interface between the "frontseat" and the "backseat". Historically, a new interface has been written for each vehicle that was to be used with MOOS-IvP (For example, the applications iHuxley, iRecon, iOceanServerComms, ...). This led to a proliferation of approaches for handling the state transitions and control. In some cases, misunderstandings involving various aspects of MOOS-IvP have led to vehicle runaways. Furthermore, as MOOS-IvP becomes even more widely adopted and the number of manufacturers of robotic assets increases, it seems sensible to minimize the duplication of effort involved in writing interfaces.
iFrontSeat aims to address these problems by providing a single open source implementation of the connection to MOOS-IvP (the "backseat") while providing a structured well-defined extensible interface for
writing different "frontseat" drivers. Currently, a driver has been developed and tested for the Bluefin Robotics family of AUVs. This talk with discuss the design of iFrontSeat with a focus on how to expand its use to a wide variety of vehicles.
Not surprisingly, a piece of software is required to interface between the "frontseat" and the "backseat". Historically, a new interface has been written for each vehicle that was to be used with MOOS-IvP (For example, the applications iHuxley, iRecon, iOceanServerComms, ...). This led to a proliferation of approaches for handling the state transitions and control. In some cases, misunderstandings involving various aspects of MOOS-IvP have led to vehicle runaways. Furthermore, as MOOS-IvP becomes even more widely adopted and the number of manufacturers of robotic assets increases, it seems sensible to minimize the duplication of effort involved in writing interfaces.
iFrontSeat aims to address these problems by providing a single open source implementation of the connection to MOOS-IvP (the "backseat") while providing a structured well-defined extensible interface for
writing different "frontseat" drivers. Currently, a driver has been developed and tested for the Bluefin Robotics family of AUVs. This talk with discuss the design of iFrontSeat with a focus on how to expand its use to a wide variety of vehicles.
Changed line 30 from:
%%
to:
%%
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!!!! %color=#7777BB% [[Talk.01-Lepage | Prev-Talk]]%% | \
to:
!!!! %color=#7777BB% [[Talk.01-LePage | Prev-Talk]]%% | \
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!!!! %color=#7777BB% [[Talk.01-Lepage|Prev-Talk]]%% | \
%color=#7777BB%[[Talk.02-Billin|Next-Talk]]%% | \
%color=#7777BB%[[Talk.02-
to:
!!!! %color=#7777BB% [[Talk.01-Lepage | Prev-Talk]]%% | \
%color=#7777BB%[[Talk.02-Schmidt | Next-Talk]]%% | \
%color=#7777BB%[[Talk.02-Schmidt | Next-Talk]]%% | \
Changed lines 23-24 from:
* Ocean Explorer UUV
* UUVs/AUVs
* UUVs/
to:
* The Ocean Explorer UUV
* Unmanned Underwater Vehicles (UUVs) / Autonomous Underwater Vehicles (AUVs)
* Unmanned Underwater Vehicles (UUVs) / Autonomous Underwater Vehicles (AUVs)
Changed line 26 from:
* New IvP Helm Behavior
to:
* IvP Helm Behavior Development
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!!!!%color=#449944% '''Kevin LePage, NATO Undersea Research Centre'''
to:
!!!!%color=#449944% '''Kevin LePage, NATO Undersea Research Centre (NURC)'''
Deleted line 21:
Added lines 23-25:
* Ocean Explorer UUV
* MOOS-IvP
* New IvP Helm Behavior
* MOOS-IvP
* New IvP Helm Behavior
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%color=#7777BB%[[Talk.02-YaariA|Next-Talk]]%% | \
to:
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!!!! %color=#7777BB% [[Talk.01-Newman|Prev-Talk]]%% | \
to:
!!!! %color=#7777BB% [[Talk.01-Lepage|Prev-Talk]]%% | \
Changed lines 11-21 from:
!! Talk-01: ''MOOS Then, Now and Next''
!!!!%color=#449944% '''Paul Newman, Oxford'''
I will provide a perspective about where MOOS came from, why I designed it as I did, where I think its
strengths lie and where I think there is room for improvement. I will describe of the range of
platforms and projects MOOS has been, is and will be used on. I won't restrict attention to the marine domain - indeed some of the most challenging deployments have been on land in particular large scale infrastructure free navigation. As I conclude I'll look ahead to the planned next substantial release of MOOS and describe the new functionality therein.
!!!!%color=#449944% '''Paul Newman, Oxford'''
I will provide a perspective about where MOOS came from, why I designed it as I did, where I think its
strengths lie and where I think there
platforms and projects MOOS has been, is and will be used on. I won't restrict attention to the marine domain - indeed some of the most challenging deployments have been on land in particular large scale infrastructure free navigation. As I conclude I'll look ahead to the planned next substantial release of MOOS and describe the new functionality therein
to:
!! Talk-01: ''Behaviour Development for Anti-Submarine Warfare: The Design of a MOOS-IvP Behavior Based on Maximizing the Doppler of Autonomous Assets Operating Within a Bistatic Sonar System''
!!!!%color=#449944% '''Kevin LePage, NATO Undersea Research Centre'''
The NATO Undersea Research Centre is currently exploring system concepts for collaborative ASW using AUVs. As part of this effort the design of autonomy algorithms (behaviours) which are adaptive on Doppler-sensitive sonar signals is being pursued. MOOS-IvP is currently used onboard two Ocean Explorer AUVs which each have horizontal line arrays and accompanying CW signal processing software capable of converting acoustic signals into time-bearing-Doppler contacts. These contacts are fused with FM contacts within NURC's DMHT tracker. The fused CW-FM tracks are acted on by the behaviours implemented within the MOOS-IvP software architecture. In this talk we explore the performance of a behaviour which seeks to maximize the future Doppler on contacts of interest. The collaborative use of this behaviour with a second vehicle performing traditional FM processing is also considered.
!!!!%color=#449944% '''Kevin LePage, NATO Undersea Research Centre'''
The NATO Undersea Research Centre is currently exploring system concepts for collaborative ASW using AUVs. As part of this effort the design of autonomy algorithms (behaviours) which are adaptive on Doppler-sensitive sonar signals is being pursued. MOOS-IvP is currently used onboard two Ocean Explorer AUVs which each have horizontal line arrays and accompanying CW signal processing software capable of converting acoustic signals into time-bearing-Doppler contacts. These contacts are fused with FM contacts within NURC's DMHT tracker. The fused CW-FM tracks are acted on by the behaviours implemented within the MOOS-IvP software architecture. In this talk we explore the performance of a behaviour which seeks to maximize the future Doppler on contacts of interest. The collaborative use of this behaviour with a second vehicle performing traditional FM processing is also considered.
Changed lines 22-24 from:
* MOOS Core
* Academia
%%
* Academia
%%
to:
* MOOS-IvP
* Anti-Submarine Warfare
%%
* Anti-Submarine Warfare
%%
Changed lines 11-12 from:
!! Talk-01: ''MOOS Updates (PLACEHOLDER)''
to:
!! Talk-01: ''MOOS Then, Now and Next''
Changed lines 15-18 from:
to:
I will provide a perspective about where MOOS came from, why I designed it as I did, where I think its
strengths lie and where I think there is room for improvement. I will describe of the range of
platforms and projects MOOS has been, is and will be used on. I won't restrict attention to the marine domain - indeed some of the most challenging deployments have been on land in particular large scale infrastructure free navigation. As I conclude I'll look ahead to the planned next substantial release of MOOS and describe the new functionality therein.
strengths lie and where I think there is room for improvement. I will describe of the range of
platforms and projects MOOS has been, is and will be used on. I won't restrict attention to the marine domain - indeed some of the most challenging deployments have been on land in particular large scale infrastructure free navigation. As I conclude I'll look ahead to the planned next substantial release of MOOS and describe the new functionality therein.
Added line 24:
* MOOS Core
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%color=#7777BB%[[Talk.ListingSorted|All-Sorted]]%%
to:
%color=#7777BB%[[Talk.ListingSorted|Talks-Sorted]]%%
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%color=#7777BB%[[Talk.Listing|All-Talks]] |
to:
%color=#7777BB%[[Talk.Listing|All-Talks]] | \
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%color=#7777BB%[[Talk.Listing|All-Talks]] |
%color=#7777BB%[[Talk.ListingSorted|All-Sorted]]%%
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!!!! %color=#7777BB% [[Talk.03-Redfield|Prev-Talk]]%% | \
%color=#7777BB%[[Talk.05-BillinGumstix|Next-Talk]]%% | \
%color=#7777BB%[[Talk.
to:
!!!! %color=#7777BB% [[Talk.01-Newman|Prev-Talk]]%% | \
%color=#7777BB%[[Talk.02-YaariA|Next-Talk]]%% | \
%color=#7777BB%[[Talk.02-YaariA|Next-Talk]]%% | \
Changed lines 10-17 from:
!! Talk-29: ''Integration and Testing of a Novel Reacquire/Identify Pattern Generation Algorithm''
!!!!%color=#449944% '''Matthew J. Bays, Jean-François Kamath and Signe A. Redfield, NSWC-PCD'''
We address the integration and field testing of a novel reacquire/identify(RID) pattern generation algorithm. This algorithm, known as Probabilistic Reacquire/ID Optimal Path Selection (PROPS), is designed to plan a path for a sidescan sonar equipped underwater vehicle in order to produce multiple views of a cluster of discrete targets. The desired pattern minimizes the total number of turns and time required, while attaining appropriate coverage of the targets. Initial tests of the pattern generation algorithm suggest that it requires between 35% and 95% of the time required by the standard “star” RID pattern. Following a brief description of the algorithm itself, we present the integration of the algorithm, both as a stand-alone MOOS module and as a library using a standard RID pattern generator created from the MOOS-IvP Helm autonomy toolkit. Simulation and field test results of the algorithm on a REMUS 100 autonomous underwater vehicle are included.
!!!!%color=#449944% '''
We address the integration and field testing of a novel reacquire/identify(RID) pattern generation algorithm. This algorithm, known as Probabilistic Reacquire/ID Optimal Path Selection (PROPS), is designed to plan a path for a sidescan sonar equipped underwater vehicle in order to produce multiple views of a cluster of discrete targets. The desired pattern minimizes the total number of turns and time required, while attaining appropriate coverage of the targets. Initial tests of the pattern generation algorithm suggest that it requires between 35% and 95% of the time required by the standard “star” RID pattern. Following a brief description of the algorithm itself, we present the integration of the algorithm, both as a stand-alone MOOS module and as a library using a standard RID pattern generator created from the MOOS-IvP Helm autonomy toolkit. Simulation and field test results of the algorithm on a REMUS 100 autonomous underwater vehicle are included
to:
!! Talk-01: ''MOOS Updates (PLACEHOLDER)''
!!!!%color=#449944% '''Paul Newman, Oxford'''
No Abstract Yet.
!!!!%color=#449944% '''Paul Newman, Oxford'''
No Abstract Yet.
Changed lines 20-24 from:
* Autonomy
* MOOS-IvP
* MCM
* UUVs
* Navy Labs
* MOOS-IvP
* MCM
* UUVs
* Navy Labs
to:
* Academia
Changed line 18 from:
!!!!%color=#BD614A% '''Categories:''' \
to:
!!!!%color=#4444BB% '''Categories:''' \
Changed line 10 from:
!! Talk-04: ''Integration and Testing of a Novel Reacquire/Identify Pattern Generation Algorithm''
to:
!! Talk-29: ''Integration and Testing of a Novel Reacquire/Identify Pattern Generation Algorithm''
Changed line 18 from:
!!!%color=#BD614A% '''Categories:''' \
to:
!!!!%color=#BD614A% '''Categories:''' \
Changed line 18 from:
%color=#BD614A% '''Categories:''' \
to:
!!!%color=#BD614A% '''Categories:''' \
Changed lines 6-7 from:
%color=#7777BB%[[Talk.05-BillinGumstix|Next-Talk]]%%
to:
%color=#7777BB%[[Talk.05-BillinGumstix|Next-Talk]]%% | \
%color=#7777BB%[[Talk.Listing|All-Talks]]%%
%color=#7777BB%[[Talk.Listing|All-Talks]]%%
Changed line 11 from:
!!!!%color=#449944% '''Matthew J. Bays, Jean- François Kamath and Signe A. Redfield, NSWC-PCD'''
to:
!!!!%color=#449944% '''Matthew J. Bays, Jean-François Kamath and Signe A. Redfield, NSWC-PCD'''
Changed line 5 from:
!!!! %color=#7777BB% [[Talk.03-Redfield|Prev-Talk]]%% |
to:
!!!! %color=#7777BB% [[Talk.03-Redfield|Prev-Talk]]%% | \
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!!!! %color=#7777BB% [[Talk.03-Redfield|Prev-Talk]]%% | [[Talk.05-BillinGumstix|Next-Talk]]
to:
!!!! %color=#7777BB% [[Talk.03-Redfield|Prev-Talk]]%% |
%color=#7777BB%[[Talk.05-BillinGumstix|Next-Talk]]%%
%color=#7777BB%[[Talk.05-BillinGumstix|Next-Talk]]%%
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!!!! %color=#444499% [[Talk.03-Redfield|Prev-Talk]]%% | [[Talk.05-BillinGumstix|Next-Talk]]
to:
!!!! %color=#7777BB% [[Talk.03-Redfield|Prev-Talk]]%% | [[Talk.05-BillinGumstix|Next-Talk]]
Changed line 5 from:
!!!! %color=#449944% [[Talk.03-Redfield|Prev-Talk]]%% | [[Talk.05-BillinGumstix|Next-Talk]]
to:
!!!! %color=#444499% [[Talk.03-Redfield|Prev-Talk]]%% | [[Talk.05-BillinGumstix|Next-Talk]]
Changed line 5 from:
[[Talk.03-Redfield|Prev-Talk]] | [[Talk.05-BillinGumstix|Next-Talk]]
to:
!!!! %color=#449944% [[Talk.03-Redfield|Prev-Talk]]%% | [[Talk.05-BillinGumstix|Next-Talk]]
Changed line 5 from:
[[Talk.04-Redfield|Prev-Talk]] | [[Talk.04-Redfield|Next-Talk]]
to:
[[Talk.03-Redfield|Prev-Talk]] | [[Talk.05-BillinGumstix|Next-Talk]]
Added lines 4-6:
[[Talk.04-Redfield|Prev-Talk]] | [[Talk.04-Redfield|Next-Talk]]
Changed line 7 from:
%color=#449944% '''Matthew J. Bays, Jean- François Kamath and Signe A. Redfield, NSWC-PCD'''
to:
!!!!%color=#449944% '''Matthew J. Bays, Jean- François Kamath and Signe A. Redfield, NSWC-PCD'''
Changed line 5 from:
!! ''Integration and Testing of a Novel Reacquire/Identify Pattern Generation Algorithm''
to:
!! Talk-04: ''Integration and Testing of a Novel Reacquire/Identify Pattern Generation Algorithm''
Changed line 13 from:
%color=#BD614A% '''Topics:''' \
to:
%color=#BD614A% '''Categories:''' \
Changed lines 5-11 from:
!! ''MOOS-Enabled Semi-Autonomous Remote USV Operations''
%color=#449944% '''Signe Redfield, NSWC-PCD'''
A multi-vehicle mission involving simultaneous identification (by UUVs) and neutralization (by a USV) of targets is complicated by the need to keep the neutralization efforts distant from the
identification vehicles. As targets are identified by the UUVs, they are relayed to the USV for imaging (proxy for neutralization). The USV plansa sequence of neutralization efforts based on desired efficiency (prosecuting targets in close proximity in the same sequence), neutralization capacity (number of targets that can be prosecuted without reloading), the location of the reloading depot, and distance from other vehicles. We present a solution to this variation of the capacitated vehicle routing problem, implemented on a semi-autonomous USV. MOOS performed the autonomous portion of the mission running on a remote laptop while a human operator ran a teleoperated underwater vehicle launched and retrieved from the USV as a proxy for the neutralization system as each target was reached. Together the system demonstrated semi-autonomous remote USV operations, with the human operator working smoothly with the autonomous system.
%color=#449944% '''
A multi-vehicle mission involving simultaneous identification (by UUVs) and neutralization (by a USV)
identification vehicles. As targets are identified by the UUVs, they are relayed to the USV for imaging (proxy for neutralization). The USV plans
to:
!! ''Integration and Testing of a Novel Reacquire/Identify Pattern Generation Algorithm''
%color=#449944% '''Matthew J. Bays, Jean- François Kamath and Signe A. Redfield, NSWC-PCD'''
We address the integration and field testing of a novel reacquire/identify(RID) pattern generation algorithm. This algorithm, known as Probabilistic Reacquire/ID Optimal Path Selection (PROPS), is designed to plan a path for a sidescan sonar equipped underwater vehicle in order to produce multiple views of a cluster of discrete targets. The desired pattern minimizes the total number of turns and time required, while attaining appropriate coverage of the targets. Initial tests of the pattern generation algorithm suggest that it requires between 35% and 95% of the time required by the standard “star” RID pattern. Following a brief description of the algorithm itself, we present the integration of the algorithm, both as a stand-alone MOOS module and as a library using a standard RID pattern generator created from the MOOS-IvP Helm autonomy toolkit. Simulation and field test results of the algorithm on a REMUS 100 autonomous underwater vehicle are included.
%color=#449944% '''Matthew J. Bays, Jean- François Kamath and Signe A. Redfield, NSWC-PCD'''
We address the integration and field testing of a novel reacquire/identify(RID) pattern generation algorithm. This algorithm, known as Probabilistic Reacquire/ID Optimal Path Selection (PROPS), is designed to plan a path for a sidescan sonar equipped underwater vehicle in order to produce multiple views of a cluster of discrete targets. The desired pattern minimizes the total number of turns and time required, while attaining appropriate coverage of the targets. Initial tests of the pattern generation algorithm suggest that it requires between 35% and 95% of the time required by the standard “star” RID pattern. Following a brief description of the algorithm itself, we present the integration of the algorithm, both as a stand-alone MOOS module and as a library using a standard RID pattern generator created from the MOOS-IvP Helm autonomy toolkit. Simulation and field test results of the algorithm on a REMUS 100 autonomous underwater vehicle are included.
Deleted line 14:
Changed line 17 from:
* Neutralization
to:
* MCM
Deleted line 18:
Changed lines 5-10 from:
!! ''Autonomous Adaptive Environmental Feature Tracking on Board AUVs: Tracking the Thermocline''
%color=#449944% '''Stephanie Petillo, MIT (LAMSS)'''
This talk addresses the challenge of autonomously and adaptively tracking features of the underwater environment using AUVs running the MOOS-IvP autonomy software. This problem is addressed from concept to implementation in the field on various AUV platforms, developing specifically the example of thermocline tracking. Some recent research involving methods for feature tracking on board multiple AUVs operating simultaneously and collaboratively to detect an underwater feature will also be discussed briefly.
%color=#449944% '''
to:
!! ''MOOS-Enabled Semi-Autonomous Remote USV Operations''
%color=#449944% '''Signe Redfield, NSWC-PCD'''
A multi-vehicle mission involving simultaneous identification (by UUVs) and neutralization (by a USV) of targets is complicated by the need to keep the neutralization efforts distant from the
identification vehicles. As targets are identified by the UUVs, they are relayed to the USV for imaging (proxy for neutralization). The USV plans a sequence of neutralization efforts based on desired efficiency (prosecuting targets in close proximity in the same sequence), neutralization capacity (number of targets that can be prosecuted without reloading), the location of the reloading depot, and distance from other vehicles. We present a solution to this variation of the capacitated vehicle routing problem, implemented on a semi-autonomous USV. MOOS performed the autonomous portion of the mission running on a remote laptop while a human operator ran a teleoperated underwater vehicle launched and retrieved from the USV as a proxy for the neutralization system as each target was reached. Together the system demonstrated semi-autonomous remote USV operations, with the human operator working smoothly with the autonomous system.
%color=#449944% '''Signe Redfield, NSWC-PCD'''
A multi-vehicle mission involving simultaneous identification (by UUVs) and neutralization (by a USV) of targets is complicated by the need to keep the neutralization efforts distant from the
identification vehicles. As targets are identified by the UUVs, they are relayed to the USV for imaging (proxy for neutralization). The USV plans a sequence of neutralization efforts based on desired efficiency (prosecuting targets in close proximity in the same sequence), neutralization capacity (number of targets that can be prosecuted without reloading), the location of the reloading depot, and distance from other vehicles. We present a solution to this variation of the capacitated vehicle routing problem, implemented on a semi-autonomous USV. MOOS performed the autonomous portion of the mission running on a remote laptop while a human operator ran a teleoperated underwater vehicle launched and retrieved from the USV as a proxy for the neutralization system as each target was reached. Together the system demonstrated semi-autonomous remote USV operations, with the human operator working smoothly with the autonomous system.
Deleted line 13:
Added line 17:
* Neutralization
Added line 19:
* USVs
Changed lines 9-10 from:
to:
This talk addresses the challenge of autonomously and adaptively tracking features of the underwater environment using AUVs running the MOOS-IvP autonomy software. This problem is addressed from concept to implementation in the field on various AUV platforms, developing specifically the example of thermocline tracking. Some recent research involving methods for feature tracking on board multiple AUVs operating simultaneously and collaboratively to detect an underwater feature will also be discussed briefly.
Changed line 13 from:
* Acoustic Communications,
to:
* Environmental Sampling
Changed lines 15-17 from:
* Autonomy%%
to:
* Autonomy
* MOOS-IvP
%%
* MOOS-IvP
%%
Changed line 7 from:
%color=#449944% '''Stephanie Petillo, MIT (LAMSS)''
to:
%color=#449944% '''Stephanie Petillo, MIT (LAMSS)'''
Changed lines 5-7 from:
!! ''Unmanned Robot Message Optimization Method (URMOM)''
%color=#449944% '''Andrew Bouchard, NSWC-PCD'''
%color=#449944% '''
to:
!! ''Autonomous Adaptive Environmental Feature Tracking on Board AUVs: Tracking the Thermocline''
%color=#449944% '''Stephanie Petillo, MIT (LAMSS)''
%color=#449944% '''Stephanie Petillo, MIT (LAMSS)''
Changed lines 11-15 from:
%color=#BD614A% '''Topics:''' Acoustic Communications, Multi-Vehicle Autonomy, Autonomy%%
to:
%color=#BD614A% '''Topics:''' \
* Acoustic Communications,
* Multi-Vehicle Autonomy
* Autonomy%%
* Acoustic Communications,
* Multi-Vehicle Autonomy
* Autonomy%%
Changed line 11 from:
''Topics:'' Acoustic Communications, Multi-Vehicle Autonomy, Autonomy
to:
%color=#BD614A% '''Topics:''' Acoustic Communications, Multi-Vehicle Autonomy, Autonomy%%
Changed lines 9-11 from:
One of the greatest challenges of working in the underwater regime is the severe limitations of acoustic communications. This problem becomes even more evident in multi-vehicle autonomy, when vehicles must continually update each other with their state and intentions to achieve cooperative goals. In order to support tests of a multi-vehicle arbiter framework, an optimization scheme was created and implemented as a MOOS module to enable sufficient message passing between vehicles. Using this tool, vehicle state and destination, shared map updates, updated algorithm parameters, target information, and decision reconciliation can be effectively shared between vehicles using the published Compact Control Language (CCL) standard for acoustic messages.
to:
One of the greatest challenges of working in the underwater regime is the severe limitations of acoustic communications. This problem becomes even more evident in multi-vehicle autonomy, when vehicles must continually update each other with their state and intentions to achieve cooperative goals. In order to support tests of a multi-vehicle arbiter framework, an optimization scheme was created and implemented as a MOOS module to enable sufficient message passing between vehicles. Using this tool, vehicle state and destination, shared map updates, updated algorithm parameters, target information, and decision reconciliation can be effectively shared between vehicles using the published Compact Control Language (CCL) standard for acoustic messages.
''Topics:'' Acoustic Communications, Multi-Vehicle Autonomy, Autonomy
''Topics:'' Acoustic Communications, Multi-Vehicle Autonomy, Autonomy
Changed line 7 from:
%color=#449944% '''Andrew Bouchard, NSWC PCD'''
to:
%color=#449944% '''Andrew Bouchard, NSWC-PCD'''
Changed line 7 from:
%color=#449944% '''Andrew Bouchard, NSWC PCD'''%
to:
%color=#449944% '''Andrew Bouchard, NSWC PCD'''
Changed line 7 from:
%color=#449944% !!! '''Andrew Bouchard, NSWC PCD'''%
to:
%color=#449944% '''Andrew Bouchard, NSWC PCD'''%
Changed line 7 from:
!!! '''Andrew Bouchard, NSWC PCD'''
to:
%color=#449944% !!! '''Andrew Bouchard, NSWC PCD'''%
Changed line 7 from:
'''Andrew Bouchard, NSWC PCD'''
to:
!!! '''Andrew Bouchard, NSWC PCD'''
Changed line 5 from:
!! Title: ''Unmanned Robot Message Optimization Method (URMOM)''
to:
!! ''Unmanned Robot Message Optimization Method (URMOM)''
Changed line 5 from:
Title: ''Unmanned Robot Message Optimization Method (URMOM)''
to:
!! Title: ''Unmanned Robot Message Optimization Method (URMOM)''
Changed lines 1-3 from:
to:
(:notitle:)
(:notitlegroup:)
(:nofooter:)
Title: ''Unmanned Robot Message Optimization Method (URMOM)''
'''Andrew Bouchard, NSWC PCD'''
(:notitlegroup:)
(:nofooter:)
Title: ''Unmanned Robot Message Optimization Method (URMOM)''
'''Andrew Bouchard, NSWC PCD'''
Changed lines 5-6 from:
severe limitations of acoustic communications. This problem becomes even more evident in multi-vehicle autonomy, when vehicles must continually update each other with their state and intentions to achieve cooperative goals. In order to support tests of a multi-vehicle arbiter framework, an optimization scheme was created and implemented as a MOOS module to enable sufficient message passing between vehicles. Using this tool, vehicle state and destination, shared map updates, updated algorithm parameters, target information, and decision reconciliation can be effectively shared between vehicles using the published Compact Control Language (CCL) standard for acoustic messages.
to:
One of the greatest challenges of working in the underwater regime is the severe limitations of acoustic communications. This problem becomes even more evident in multi-vehicle autonomy, when vehicles must continually update each other with their state and intentions to achieve cooperative goals. In order to support tests of a multi-vehicle arbiter framework, an optimization scheme was created and implemented as a MOOS module to enable sufficient message passing between vehicles. Using this tool, vehicle state and destination, shared map updates, updated algorithm parameters, target information, and decision reconciliation can be effectively shared between vehicles using the published Compact Control Language (CCL) standard for acoustic messages.
Changed lines 5-14 from:
One of the greatest challenges of working in the underwater regime is the \
severe limitations of acoustic communications. This problem becomes even moree\
vident in multi-vehicle autonomy, when vehicles must continually update each ot\
her with their state and intentions to achieve cooperative goals. In order to s\
upport tests of a multi-vehicle arbiter framework, an optimization scheme was c\
reated and implemented as a MOOS module to enable sufficient message passing be\
tween vehicles. Using this tool, vehicle state and destination, shared map upda\
tes, updated algorithm parameters, target information, and decision reconciliat\
ion can be effectively shared between vehicles using the published Compact Cont\
rol Language (CCL) standard for acoustic messages.
severe limitations of acoustic communications. This problem becomes even more
vident
her
upport
reated
tween
tes
ion
rol
to:
One of the greatest challenges of working in the underwater regime is the
severe limitations of acoustic communications. This problem becomes even more evident in multi-vehicle autonomy, when vehicles must continually update each other with their state and intentions to achieve cooperative goals. In order to support tests of a multi-vehicle arbiter framework, an optimization scheme was created and implemented as a MOOS module to enable sufficient message passing between vehicles. Using this tool, vehicle state and destination, shared map updates, updated algorithm parameters, target information, and decision reconciliation can be effectively shared between vehicles using the published Compact Control Language (CCL) standard for acoustic messages.
severe limitations of acoustic communications. This problem becomes even more evident in multi-vehicle autonomy, when vehicles must continually update each other with their state and intentions to achieve cooperative goals. In order to support tests of a multi-vehicle arbiter framework, an optimization scheme was created and implemented as a MOOS module to enable sufficient message passing between vehicles. Using this tool, vehicle state and destination, shared map updates, updated algorithm parameters, target information, and decision reconciliation can be effectively shared between vehicles using the published Compact Control Language (CCL) standard for acoustic messages.
Added lines 1-14:
Title: Unmanned Robot Message Optimization Method (URMOM)
Andrew Bouchard, NSWC PCD
One of the greatest challenges of working in the underwater regime is the \
severe limitations of acoustic communications. This problem becomes even more e\
vident in multi-vehicle autonomy, when vehicles must continually update each ot\
her with their state and intentions to achieve cooperative goals. In order to s\
upport tests of a multi-vehicle arbiter framework, an optimization scheme was c\
reated and implemented as a MOOS module to enable sufficient message passing be\
tween vehicles. Using this tool, vehicle state and destination, shared map upda\
tes, updated algorithm parameters, target information, and decision reconciliat\
ion can be effectively shared between vehicles using the published Compact Cont\
rol Language (CCL) standard for acoustic messages.
Andrew Bouchard, NSWC PCD
One of the greatest challenges of working in the underwater regime is the \
severe limitations of acoustic communications. This problem becomes even more e\
vident in multi-vehicle autonomy, when vehicles must continually update each ot\
her with their state and intentions to achieve cooperative goals. In order to s\
upport tests of a multi-vehicle arbiter framework, an optimization scheme was c\
reated and implemented as a MOOS module to enable sufficient message passing be\
tween vehicles. Using this tool, vehicle state and destination, shared map upda\
tes, updated algorithm parameters, target information, and decision reconciliat\
ion can be effectively shared between vehicles using the published Compact Cont\
rol Language (CCL) standard for acoustic messages.