Talk
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.
02-Anderson
Talk.02-Anderson History
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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:
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. [[Path:/moos-dawg15/docs/S02-Anderson.pdf | %color=#ff7f00% DOWNLOAD]]
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* Payload Autonomy Interface
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* UUVs
* MCM
* Simulation/Visualization
* MCM
* Simulation/Visualization
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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'''
Thefuture 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% '''
The
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'''
!!!!%color=#449944% '''David G. Johnson, Australian Centre for Field Robotics, The University of Sydney'''
!!!!%color=#449944% '''David Battle, Defence Science and Technology Organisation'''
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'''
!!!!%color=#449944% '''David G. Johnson, Australian Centre for Field Robotics, The University of Sydney'''
!!!!%color=#449944% '''David Battle, Defence Science and Technology Organisation'''
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.
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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'''
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!!!!%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.
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* Bluefin Robotics
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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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* 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
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* 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.
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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.
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* Ocean Explorer UUV
* UUVs/AUVs
* UUVs/
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* The Ocean Explorer UUV
* Unmanned Underwater Vehicles (UUVs) / Autonomous Underwater Vehicles (AUVs)
* Unmanned Underwater Vehicles (UUVs) / Autonomous Underwater Vehicles (AUVs)
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* New IvP Helm Behavior
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* IvP Helm Behavior Development
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!!!!%color=#449944% '''Kevin LePage, NATO Undersea Research Centre'''
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!!!!%color=#449944% '''Kevin LePage, NATO Undersea Research Centre (NURC)'''
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* Ocean Explorer UUV
* MOOS-IvP
* New IvP Helm Behavior
* MOOS-IvP
* New IvP Helm Behavior
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!! 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.
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* MOOS Core
* Academia
%%
* Academia
%%
to:
* MOOS-IvP
* Anti-Submarine Warfare
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* Anti-Submarine Warfare
%%
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!! Talk-01: ''MOOS Updates (PLACEHOLDER)''
to:
!! Talk-01: ''MOOS Then, Now and Next''
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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.
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* 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]]%%
to:
%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]]%% | \
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!! 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
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!!!!%color=#BD614A% '''Categories:''' \
to:
!!!!%color=#4444BB% '''Categories:''' \
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!! 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''
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!!!%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'''
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!!!! %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]]
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!!!! %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]]
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%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''
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%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.
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* Neutralization
to:
* MCM
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!! ''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:
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* Neutralization
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* 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
%%
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%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%%
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''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'''%
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!!! '''Andrew Bouchard, NSWC PCD'''
to:
%color=#449944% !!! '''Andrew Bouchard, NSWC PCD'''%
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'''Andrew Bouchard, NSWC PCD'''
to:
!!! '''Andrew Bouchard, NSWC PCD'''
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!! 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.