Talk

20-Sideleau

Talk.20-Sideleau History

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!!!!%color=#449944% '''Michael Conry, Andrew Keefe, Halie Murray-Davis, Boston Engineering'''
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!!!!%color=#449944% '''Philip Igoe, UMass Dartmouth'''
!!!!%color=#449944% '''
Dr. Ramprasad Balasubramanian, UMass Dartmouth'''
to:
!!!!%color=#449944% '''Philip Igoe, Dr. Ramprasad Balasubramanian, UMass Dartmouth'''
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!!!!%color=#449944% '''Scott R. Sideleau, Naval Undersea Warfare Center, Division Newport RI.[[<<]]'''
%color=#449944% '''Philip Igoe, UMass Dartmouth'''
to:
!!!!%color=#449944% '''Scott R. Sideleau, Naval Undersea Warfare Center, Division Newport RI.'''
!!!!%color=#449944% '''Philip Igoe, UMass Dartmouth'''
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!!!!%color=#449944% '''Scott R. Sideleau, Naval Undersea Warfare Center, Division Newport RI.'''
!!!!%color=#449944% '''Philip Igoe, UMass Dartmouth'''
to:
!!!!%color=#449944% '''Scott R. Sideleau, Naval Undersea Warfare Center, Division Newport RI.[[<<]]'''
%color=#449944% '''Philip Igoe, UMass Dartmouth'''
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!!!!%color=#449944% '''Scott R. Sideleau, Philip Igoe, Dr. Ramprasad Balasubramanian'''
to:
!!!!%color=#449944% '''Scott R. Sideleau, Naval Undersea Warfare Center, Division Newport RI.'''
!!!!%color=#449944% '''Philip Igoe, UMass Dartmouth'''
!!!!%color=#449944% '''Dr. Ramprasad Balasubramanian, UMass Dartmouth
'''
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* MOOS-IvP
to:
* Interoperability
* UUVs
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!!!! %color=#7777BB% [[Talk.19-Carrig | Prev-Talk]]%%  | \
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%color=#7777BB%[[Talk.ListingSorted|Talks-Sorted]]%% 

!! Talk
-20: ''MOOS-IvP 17.7 - What's New, and What's Planned''

!!!!%color=#449944% '''Michael Benjamin, MIT'''

MOOS-IvP Release 17
.7 contains new applications, new behaviors and improvements to previously released modules. This talk will describe these new components and we will discuss plans for upcoming features and applications for the next release.
to:
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%color=#7777BB%[[Main.HomePage|MOOS
-DAWG'19]]%%

!! Talk
-20: ''Exploring the Use of MOOS-IvP on Biomimetic AUVs: A Case Study with Boston Engineering’s BioSwimmer''

!!!!%color=#449944% '''Scott R. Sideleau, Philip Igoe, Dr. Ramprasad Balasubramanian'''

UMass Dartmouth has developed, in coordination with Boston Engineering, a MOOS-IvP interface application for the BioSwimmer AUV. The design and development of the iBioSwimmer application is reviewed and initial in-water demonstration results presented. The BioSwimmer AUV has a unique biomimetic design that features a caudal fin that can be articulated across a range of motion. Proposed follow-on research to exploit this unique appendage and its potential applications are discussed
.
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!!!!%color=#449944% '''Michael Benjamin, MIT'''
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!!!!%color=#449944% '''Michael Benjamin, MIT'''
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MOOS-IvP Release 17.7 contains new applications, new behaviors and improvements to previously released modules. This talk will describe these new components and we will discuss plans for upcoming features and applications for the next release.
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!!!! %color=#7777BB% [[Talk.19-Rypkema | Prev-Talk]]%%  | \
%color=#7777BB%[[Talk.21-Paull | Next-Talk]]%% | \
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!!!! %color=#7777BB% [[Talk.19-Carrig | Prev-Talk]]%%  | \
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!! Talk-20: ''The Riptide UUV: A New Modular, Open, Low-Cost Micro-UUV''

!!!!%color=#449944% '''Jeff Smith, Riptide'''

Unmanned Undersea Vehicles (UUVs) have traditionally been expensive tools used for oceanographic research, naval operations, search and salvage, and commercial exploration and inspection. Architectures have developed that provided benefits to research and development for more open interfaces, flexible control, and modular, swappable subsystems. Riptide Autonomous Solutions, leveraging heavily from the exploding commercial electronics and automation markets, have developed a new, state-of-the-art, low cost development solution ideally suited for developers of autonomy and behaviors, power systems, subsea sensors, and new payloads. The micro-UUV features open hardware and software interfaces to provide users a reliable and robust platform to advance technology development. The vehicle design is optimized for high efficiency with the best hydrodynamic signature in its class.

to:
!! Talk-20: ''MOOS-IvP 17.7 - What's New, and What's Planned''

!!!!%color=#449944% '''Michael Benjamin, MIT'''
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* AUVs
to:
* MOOS-IvP
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!! Talk-20: ''The Riptide uUUV: A New Modular, Open, Low-Cost Micro-UUV''
to:
!! Talk-20: ''The Riptide UUV: A New Modular, Open, Low-Cost Micro-UUV''
Changed line 10 from:
!! Talk-20: ''The Riptide UUV: A New Modular, Open, Low-Cost Micro-UUV''
to:
!! Talk-20: ''The Riptide uUUV: A New Modular, Open, Low-Cost Micro-UUV''
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!! Talk-20: ''Modular, Open, Low-Cost Micro-UUVs to Support Development''
to:
!! Talk-20: ''The Riptide UUV: A New Modular, Open, Low-Cost Micro-UUV''
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!!!! %color=#7777BB% [[Talk.17-Defilippo | Prev-Talk]]%%  | \
%color=#7777BB%[[Talk.19-Rypkema | Next-Talk]]%% | \
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!!!! %color=#7777BB% [[Talk.19-Rypkema | Prev-Talk]]%%  | \
%color=#7777BB%[[Talk.20-Smith | Next-Talk]]%% | \
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!! Talk-18: ''SandShark: An Open Platform for Rapid Technology Development''

!!!!%color=#449944% '''Mike DeFilippo, MIT Sea Grant'''

The Bluefin SandShark is a small, open-platform, autonomous underwater vehicle (AUV) designed for scientists and developers.  SandShark combines a standardized low-cost tail with core vehicle systems, a large modular payload area, and an open development platform. This combination provides a flexible subsea "reference design" to support rapid technology development.

SandShark is built on open source software and allows end users to modify or replace all aspects of the vehicle?s software stack. Features like quick-connect fins for tool-less field replacement
and modular body components make SandShark fast and easy to modify. SandShark's payload bay makes up over half the vehicle - it is the ideal test platform for small subsea sensors and autonomy scenarios.
to:
!! Talk-20: ''Modular, Open, Low-Cost Micro-UUVs to Support Development''

!!!!%color=#449944% '''Jeff Smith, Riptide'''

Unmanned Undersea Vehicles (UUVs) have traditionally been expensive tools used for oceanographic research, naval operations, search and salvage, and commercial exploration and inspection. Architectures have developed that provided benefits to research and development for more open interfaces, flexible control, and modular, swappable subsystems. Riptide Autonomous Solutions, leveraging heavily from the exploding commercial electronics and automation markets, have developed a new, state-of-the-art, low cost development solution ideally suited for developers of autonomy and behaviors, power systems, subsea sensors, and new payloads. The micro-UUV features open hardware and software interfaces to provide users a reliable and robust platform to advance technology development. The vehicle design is optimized for high efficiency with the best hydrodynamic signature in its class.
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* ASVs
* Seafloor Surveying
to:
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!!!! %color=#7777BB% [[Talk.16-Dorsett | Prev-Talk]]%%  | \
%color=#7777BB%[[Talk.18-Murphy | Next-Talk]]%% | \
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!!!! %color=#7777BB% [[Talk.17-Defilippo | Prev-Talk]]%%  | \
%color=#7777BB%[[Talk.19-Rypkema | Next-Talk]]%% | \
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!! Talk-17: ''A Brief History of MIT Sea Grant's Remote Explorer (REx): Reef Explorer (REx I-III) and the New Remote Explorer (REx IV)''
to:
!! Talk-18: ''SandShark: An Open Platform for Rapid Technology Development''
Changed lines 14-16 from:
The Reef Explorer program was developed by the AUV Laboratory at the MIT Sea Grant College Program in 2008. The Reef Explorer program was developed with the purpose of remote education and remote research of coral reefs in Hawaii. Anywhere from the surface to a depth of 25 meters the REx was able to sample the environment with multiple marine sensors and capture video data with onboard cameras. The data and video was then streamed in real time through a radio modem to a nearby relay station and then to the internet. This architecture enabled two way real time communications between a computer on land and the robot underwater. The next iteration of the Reef Explorer program will be the Remote Explorer (REx IV). The Remote Explorer program will not only be a way to expand ocean science to the classroom, but also to provide a platform for real-time data collection for the greater scientific community.
to:
The Bluefin SandShark is a small, open-platform, autonomous underwater vehicle (AUV) designed for scientists and developers.  SandShark combines a standardized low-cost tail with core vehicle systems, a large modular payload area, and an open development platform. This combination provides a flexible subsea "reference design" to support rapid technology development.

SandShark is built on open source software and allows end users to modify or replace all aspects of the vehicle?s software stack. Features like quick-connect fins for tool-less field replacement and modular body components make SandShark fast and easy to modify. SandShark's payload bay makes up over half the vehicle - it is the ideal test platform for small subsea sensors and autonomy scenarios
.
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!!!! %color=#7777BB% [[Talk.15-McKibbin | Prev-Talk]]%%  | \
%color=#7777BB%[[Talk.16-Dorsett | Next-Talk]]%% | \
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!!!! %color=#7777BB% [[Talk.16-Dorsett | Prev-Talk]]%%  | \
%color=#7777BB%[[Talk.18-Murphy | Next-Talk]]%% | \
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!! Talk-16: ''MOOS Chat Down Under''

!!!!%color=#449944% '''Helen Dorsett, Australian Defence Science and Technology Organisation'''

Over Christmas break this year, the Australian Defence Science and Technology Organisation sponsored a
'summer vacation scholar' from the University of Adelaide to develop an Android-based smart phone app that would enable hand-held communications with an underwater robot, the REMUS 100.

Fuelled mainly by "wouldn't it be cool?,"
the project evolved into two products: an "underwater Tom-Tom" for in-situ mission monitoring via a Bluetooth-enabled link to a REMUS Ranger unit; and post-mission "chat" via WiFi, which displays "objects of interest" the vehicle found during a survey mission that the operator could then select for further mission re-tasking.

Both products were successfully tested in Jervis Bay in late February alongside a naval mine countermeasures exercise, and the results go a long
way towards demonstrating scientifically what we've always suspected - that in some situations, real-time and near-real-time communications with autonomous underwater vehicles are well orth the effort.

In this presentation, we'll show how we did it with MOOS and our REMUS 'autonomy nose,' along with some discussion of more recent developments
.

to:
!! Talk-17: ''A Brief History of MIT Sea Grant's Remote Explorer (REx): Reef Explorer (REx I-III) and the New Remote Explorer (REx IV)''

!!!!%color=#449944% '''Mike DeFilippo, MIT Sea Grant'''

The Reef Explorer program was developed by the AUV Laboratory at the MIT Sea Grant College Program in 2008
. The Reef Explorer program was developed with the purpose of remote education and remote research of coral reefs in Hawaii. Anywhere from the surface to a depth of 25 meters the REx was able to sample the environment with multiple marine sensors and capture video data with onboard cameras. The data and video was then streamed in real time through a radio modem to a nearby relay station and then to the internet. This architecture enabled two way real time communications between a computer on land and the robot underwater. The next iteration of the Reef Explorer program will be the Remote Explorer (REx IV). The Remote Explorer program will not only be a way to expand ocean science to the classroom, but also to provide a platform for real-time data collection for the greater scientific community.
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* UUVs
* Command and Control, Mission Planning
* Simulation/Visualization
* Communications
to:
* AUVs
* ASVs
* Seafloor Surveying
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* Payload Autonomy Interface
to:
* UUVs
* Command and Control, Mission Planning
* Simulation/Visualization
* Communications
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!!!! %color=#7777BB% [[Talk.11-Seto | Prev-Talk]]%%  | \
%color=#7777BB%[[Talk.13-Schneider | Next-Talk]]%% | \
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!!!! %color=#7777BB% [[Talk.15-McKibbin | Prev-Talk]]%%  | \
%color=#7777BB%[[Talk.16-Dorsett | Next-Talk]]%% | \
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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.
to:
!! Talk-16: ''MOOS Chat Down Under''

!!!!%color=#449944% '''Helen Dorsett, Australian Defence Science and Technology Organisation'''

Over Christmas break this year, the Australian Defence Science and Technology Organisation sponsored a 'summer vacation scholar' from the University of Adelaide to develop an Android-based smart phone app that would enable hand-held communications with an underwater robot, the REMUS 100.

Fuelled mainly by "wouldn't it be cool?," the project evolved into two products: an "
underwater Tom-Tom" for in-situ mission monitoring via a Bluetooth-enabled link to a REMUS Ranger unit; and post-mission "chat" via WiFi, which displays "objects of interest" the vehicle found during a survey mission that the operator could then select for further mission re-tasking.

Both products were successfully tested in Jervis Bay in late February alongside a naval mine countermeasures exercise, and the results go
a long way towards demonstrating scientifically what we've always suspected - that in some situations, real-time and near-real-time communications with autonomous underwater vehicles are well orth the effort.

In this presentation, we'll show how we did it with MOOS
and our REMUS 'autonomy nose,' along with some discussion of more recent developments.
Changed lines 5-6 from:
!!!! %color=#7777BB% [[Talk.10-Keane | Prev-Talk]]%%  | \
%color=#7777BB%[[Talk.12-Seto | Next-Talk]]%% | \
to:
!!!! %color=#7777BB% [[Talk.11-Seto | Prev-Talk]]%%  | \
%color=#7777BB%[[Talk.13-Schneider | Next-Talk]]%% | \
Changed lines 11-12 from:
!! Talk-11: ''Implementing on-line AUV SLAM using side-scan sonar and automated target detection with MOOS- IvP''
to:
!! Talk-12: ''Data association towards persistent SLAM with underwater sideScan sonar implemented in MOOS-IvP''
Added lines 15-16:
!!!!%color=#449944% '''Colin MacKenzie'''
Deleted lines 18-19:
!!!!%color=#449944% '''Colin MacKenzie'''
Changed lines 21-25 from:
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
.
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
.
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!!!! %color=#7777BB% [[Talk.09-ValSchmidt | Prev-Talk]]%%  | \
%color=#7777BB%[[Talk.11-Seto | Next-Talk]]%% | \
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!!!! %color=#7777BB% [[Talk.10-Keane | Prev-Talk]]%%  | \
%color=#7777BB%[[Talk.12-Seto | Next-Talk]]%% | \
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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-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.
 
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.
Changed lines 5-6 from:
!!!! %color=#7777BB% [[Talk.07-Kutzke | Prev-Talk]]%%  | \
%color=#7777BB%[[Talk.04-Gagner | Next-Talk]]%% | \
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!!!! %color=#7777BB% [[Talk.09-ValSchmidt | Prev-Talk]]%%  | \
%color=#7777BB%[[Talk.11-Seto | Next-Talk]]%% | \
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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.
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
.
Changed line 5 from:
!!!! %color=#7777BB% [[Talk.06-Sideleau | Prev-Talk]]%%  | \
to:
!!!! %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'''
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.
Deleted line 16:
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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!!!! %color=#7777BB% [[Talk.02-Anderson | Prev-Talk]]%%  | \
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!!!! %color=#7777BB% [[Talk.06-Sideleau | Prev-Talk]]%%  | \
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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.
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.
Changed lines 5-6 from:
!!!! %color=#7777BB% [[Talk.01-Schneider | Prev-Talk]]%%  | \
%color=#7777BB%[[Talk.02-Purvis | Next-Talk]]%% | \
to:
!!!! %color=#7777BB% [[Talk.02-Anderson | Prev-Talk]]%%  | \
%color=#7777BB%[[Talk.04-Gagner | Next-Talk]]%% | \
Changed lines 15-20 from:
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.

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.
Changed lines 11-17 from:
!! 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
'''
to:
!! Talk-03: ''Cross-Domain UxV Collaboration Scenario Development with MOOS-IvP''

!!!!%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''

!!!!%color=#449944% ''' 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'''
Added lines 14-17:

!!!!%color=#449944% '''David G. Johnson, Australian Centre for Field Robotics, The University of Sydney''

!!!!%color=#449944% ''' David Battle, Defence Science and Technology Organisation'''
Changed lines 11-23 from:
!! 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,
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 will discuss the design of iFrontSeat with a focus on how to expand its use to a wide variety of
vehicles.

[[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.

Changed lines 25-26 from:
* Vehicle Safety
* Bluefin Robotics
to:
Added lines 21-22:

[[Path:/moos-dawg13/docs/01-schneider_brief.pdf | %color=#ff7f00% DOWNLOAD]]
Changed line 20 from:
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.
Added line 25:
* Vehicle Safety
Changed lines 5-6 from:
!!!! %color=#7777BB% [[Talk.01-LePage | Prev-Talk]]%%  | \
%color=#7777BB%[[Talk.02-Schmidt | Next-Talk]]%% | \
to:
!!!! %color=#7777BB% [[Talk.01-Schneider | Prev-Talk]]%%  | \
%color=#7777BB%[[Talk.02-Purvis | Next-Talk]]%% | \
Changed lines 24-29 from:
* Anti-Submarine Warfare
*
Autonomy / Collaborative Autonomy
* The Ocean Explorer UUV
* Unmanned Underwater Vehicles (UUVs) / Autonomous Underwater Vehicles (AUVs)
* MOOS-IvP
* IvP Helm Behavior Development
to:
* Payload Autonomy Interface
* Bluefin Robotics

Changed lines 11-15 from:
!! 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.
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.
Changed lines 15-19 from:
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.



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.

Changed line 30 from:
%%
to:
%%
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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]]%% | \
to:
!!!! %color=#7777BB% [[Talk.01-Lepage | Prev-Talk]]%%  | \
%color=#7777BB%[[Talk.02-Schmidt | Next-Talk]]%% | \
Added line 23:
* Autonomy / Collaborative Autonomy
Changed lines 23-24 from:
* Ocean Explorer UUV
* UUVs/AUVs
to:
* The Ocean Explorer UUV
* Unmanned Underwater Vehicles (UUVs) / Autonomous Underwater Vehicles (AUVs)
Changed line 26 from:
* New IvP Helm Behavior
to:
* IvP Helm Behavior Development
Changed line 13 from:
!!!!%color=#449944% '''Kevin LePage, NATO Undersea Research Centre'''
to:
!!!!%color=#449944% '''Kevin LePage, NATO Undersea Research Centre (NURC)'''
Added line 24:
* UUVs/AUVs
Deleted line 21:
* MOOS-IvP
Added lines 23-25:
* Ocean Explorer UUV
* MOOS-IvP
* New IvP Helm Behavior
Changed line 6 from:
%color=#7777BB%[[Talk.02-YaariA|Next-Talk]]%% | \
to:
%color=#7777BB%[[Talk.02-Billin|Next-Talk]]%% | \
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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
.



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.



Changed lines 22-24 from:
* MOOS Core
* Academia

%%
to:
* MOOS-IvP
* 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:
No Abstract Yet.


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.




Added line 24:
* MOOS Core
Changed line 8 from:
%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]] | \
Changed lines 7-8 from:
%color=#7777BB%[[Talk.Listing|All-Talks]]%% 
to:
%color=#7777BB%[[Talk.Listing|All-Talks]] |
%color=#7777BB%[[Talk.ListingSorted|All-Sorted
]]%% 
Changed lines 5-6 from:
!!!! %color=#7777BB% [[Talk.03-Redfield|Prev-Talk]]%%  | \
%color=#7777BB%[[Talk.05-BillinGumstix|Next-Talk]]%% | \
to:
!!!! %color=#7777BB% [[Talk.01-Newman|Prev-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
.


to:
!! Talk-01: ''MOOS Updates (PLACEHOLDER)''

!!!!%color=#449944% '''Paul Newman, Oxford'''

No Abstract Yet
.


Changed lines 20-24 from:
* Autonomy
* 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
]]%% 
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]]%%  | \
Changed lines 5-6 from:
!!!! %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]]%% 
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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]] 

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:''' \
Added line 19:
* Navy Labs
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 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.
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.


Deleted line 14:
* Multi-Vehicle Autonomy
Changed line 17 from:
* Neutralization
to:
* MCM
Deleted line 18:
* USVs
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 trackingSome 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.
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.
Deleted line 13:
* Environmental Sampling
Added line 17:
* Neutralization
Added line 19:
* USVs
Added line 17:
* UUVs
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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:
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.
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* Acoustic Communications,
to:
* Environmental Sampling
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* Autonomy%%
to:
* Autonomy
* MOOS-IvP
%%
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%color=#449944% '''Stephanie Petillo, MIT (LAMSS)''
to:
%color=#449944% '''Stephanie Petillo, MIT (LAMSS)'''
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!! ''Unmanned Robot Message Optimization Method (URMOM)''

%color=#449944% '''Andrew Bouchard, NSWC-PCD'''
to:
!! ''Autonomous Adaptive Environmental Feature Tracking on Board AUVs: Tracking the Thermocline''

%color=#449944% '''Stephanie Petillo, MIT (LAMSS)''
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%color=#BD614A% '''Topics:''' Acoustic Communications, Multi-Vehicle Autonomy, Autonomy%%
to:
%color=#BD614A% '''Topics:''' \

*
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%%
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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
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%color=#449944% '''Andrew Bouchard, NSWC PCD'''
to:
%color=#449944% '''Andrew Bouchard, NSWC-PCD'''
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%color=#449944% '''Andrew Bouchard, NSWC PCD'''%
to:
%color=#449944% '''Andrew Bouchard, NSWC PCD'''
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%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)''
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Title: ''Unmanned Robot Message Optimization Method (URMOM)''
to:
!! Title: ''Unmanned Robot Message Optimization Method (URMOM)''
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Title: Unmanned Robot Message Optimization Method (URMOM)

Andrew Bouchard, NSWC PCD
to:
(:notitle:)
(:notitlegroup:)
(:nofooter:)

Title: ''Unmanned Robot Message Optimization Method (URMOM)''

'''Andrew Bouchard, NSWC PCD'''
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     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.
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 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.
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.
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.