Talk

05-Slaets

Talk.05-Slaets History

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July 12, 2019, at 04:21 PM by 128.30.27.150 -
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!!!!%color=#449944% '''Peter Slaets, Gerben Peeters, Senne Van Baelen University of Leuven, KU Leuven, Belgium'''
to:
!!!!%color=#449944% '''Speaker: Peter Slaets, University of Leuven, KU Leuven, Belgium'''

!!!!%color=#449944% '''
Gerben Peeters, Senne Van Baelen University of Leuven, KU Leuven, Belgium'''
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* OpenCPN
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The IMP research group at KU Leuven, Belgium is performing research within the domain of autonomous Inland shipping. For this reason MOOS-ivp was selected as middle-ware platform in 2017 and during the upcoming years extended with additional obstacles management functionalities and more adequate visualization. These extensions make MOOS-ivp applicable for (basic) inland waterway maneuvering. The concept of static and dynamic obstacles is introduced and integrated within MOOS-ivp enabling confined space navigation based on the current ivp-solver. Challenges and opportunities induced by this new approach are addresses more in detail. The current pMarineViewer is replaced by an open source openCPN viewer that fully exploits the additional features of the public available Electronic Navigation Charts (ENCs). The limitation of the current viewer and the opportunities when using openCPN are addressed.
to:
The IMP research group at KU Leuven, Belgium is performing research within the domain of autonomous Inland shipping. For this reason MOOS-IvP was selected as middle-ware platform in 2017 and during the upcoming years extended with additional obstacles management functionalities and more adequate visualization. These extensions make MOOS-IvP applicable for (basic) inland waterway maneuvering. The concept of static and dynamic obstacles is introduced and integrated within MOOS-IvP enabling confined space navigation based on the current ivp-solver. Challenges and opportunities induced by this new approach are addresses more in detail. The current pMarineViewer is replaced by an open source openCPN viewer that fully exploits the additional features of the public available Electronic Navigation Charts (ENCs). The limitation of the current viewer and the opportunities when using openCPN are addressed.
Changed lines 20-21 from:
* Command and Control
* Open Standards
to:
* USVs
* Inland USVs
* Mission Control
Changed line 12 from:
!! Talk-05: ''MOOS-IvP for autonomous inland shipping: Challenges and Opportunities''
to:
!! Talk-05: ''MOOS-IvP for Autonomous Inland Shipping: Challenges and Opportunities''
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!! Talk-05: ''MOOS-I-P for autonomous inland shipping: Challenges and Opportunities''
to:
!! Talk-05: ''MOOS-IvP for autonomous inland shipping: Challenges and Opportunities''
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!!!! %color=#7777BB% [[Talk.04-Zeng | Prev-Talk]]%%  | \
%color=#7777BB%[[Talk.06-Sideleau | Next-Talk]]%% | \
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!!!! %color=#7777BB% [[Talk.04-Donlan | Prev-Talk]]%%  | \
%color=#7777BB%[[Talk.06-Rimmer | Next-Talk]]%% | \
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!! Talk-05: ''Enabling Open Standards in Maritime Robotics Research with MOOS-IvP''

!!!!%color=#449944% '''Scott R. Sideleau, Christopher W. Gagner, Matthew J. Ferro, Dr. Michael L. Incze, NUWC-DIVNPT'''

There is a desire to enable the Command & Control (C2) of unmanned systems using open standards to reduce operating costs and increase interoperability in a system of systems environment. This work reports on the use of MOOS-IvP in several recent field experiments to enable the use of commercial off-the-shelf cross-domain unmanned systems (i.e. UAVs, UUVs) via an open standards mediation layer employing SAE's Joint Architecture for Unmanned Systems (JAUS) messaging.  The overall capability is enhanced across field experimentation as the themes of
battlespace characterizing behavior development,
in-stride data processing, data exfiltration, common control, and handover are explored.  The development of private services and messaging using the engineering tools provided by the open standard, which are then proposed to the standards body, is reported upon and discussed.


to:
!! Talk-05: ''MOOS-I-P for autonomous inland shipping: Challenges and Opportunities''

!!!!%color=#449944% '''Peter Slaets, Gerben Peeters, Senne Van Baelen University of Leuven, KU Leuven, Belgium'''

The IMP research group at KU Leuven, Belgium is performing research within the domain of autonomous Inland shipping. For this reason MOOS-ivp was selected as middle-ware platform in 2017 and during the upcoming years extended with additional obstacles management functionalities and more adequate visualization. These extensions make MOOS-ivp applicable for (basic) inland waterway maneuvering. The concept of static and dynamic obstacles is introduced and integrated within MOOS-ivp enabling confined space navigation based on the current ivp-solver. Challenges and opportunities induced by this new approach are addresses more in detail. The current pMarineViewer is replaced by an open source openCPN viewer that fully exploits the additional features of the public available Electronic Navigation Charts (ENCs). The limitation of the current viewer and the opportunities when using openCPN are addressed.
Deleted lines 20-22:
* UAVs
* AUVs
* JAUS
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!!!! %color=#7777BB% [[Talk.04-Gagner | Prev-Talk]]%%  | \
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!!!! %color=#7777BB% [[Talk.04-Zeng | Prev-Talk]]%%  | \
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!! Talk-05: ''MOOS-IvP in an Enterprise Linux Environment''

!!!!%color=#449944% '''Scott R. Sideleau, Christopher W. Gagner, NUWC-DIVNPT'''

As the maritime robotics community's platforms of interest mature, the churn associated with
the use of "bleeding edge" Linux distributions – often popular in academia – becomes unpalatable; often such choices lead to adding significant cost and schedule risk to projects seeking to minimize surprises during integration.  In addition, this model provides no mechanism to facilitate backwards compatibility between new and old components.  The Department of Defense (DoD) and particularly the Department of the Navy (DoN) have chosen to standardize on Red Hat Enterprise Linux and its close variants, where feasible.  This works reports on applied research at NUWC Newport that has fielded development environments based on Red Hat Enterprise Linux 6 to users and compatible Community Enterprise Operating System (CentOS) 6 environments to maritime robotic platforms.  We present our lessons learned from making the switch, discuss the methodology for correctly backporting required packages to get MOOS-IvP and Gobysoft applications up-and-running correctly, and future work focused on increasing backwards compatibility amongst our systems.

to:
!! Talk-05: ''Enabling Open Standards in Maritime Robotics Research with MOOS-IvP''

!!!!%color=#449944% '''Scott R. Sideleau, Christopher W. Gagner, Matthew J. Ferro, Dr. Michael L. Incze, NUWC-DIVNPT'''

There is a desire to enable
the Command & Control (C2) of unmanned systems using open standards to reduce operating costs and increase interoperability in a system of systems environment. This work reports on the use of MOOS-IvP in several recent field experiments to enable the use of commercial off-the-shelf cross-domain unmanned systems (i.e. UAVs, UUVs) via an open standards mediation layer employing SAE's Joint Architecture for Unmanned Systems (JAUS) messaging.  The overall capability is enhanced across field experimentation as the themes of
battlespace characterizing behavior development, in-stride data processing, data exfiltration, common control, and handover are explored.  The development of private services and messaging using the engineering tools provided by the open standard, which are then proposed
to the standards body, is reported upon and discussed.


Changed lines 22-26 from:
* Build/Integration
to:
* Command and Control
* Open Standards
* UAVs
* AUVs
* JAUS
Changed line 20 from:
* Payload Autonomy Interface
to:
* Build/Integration
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!!!! %color=#7777BB% [[Talk.02-Anderson | Prev-Talk]]%%  | \
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!!!! %color=#7777BB% [[Talk.04-Gagner | 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-05: ''MOOS-IvP in an Enterprise Linux Environment''

!!!!%color=#449944% '''Scott R. Sideleau, Christopher W. Gagner, NUWC-DIVNPT'''

As the maritime robotics community's platforms of interest mature, the churn associated with the use of "bleeding edge" Linux distributions – often popular in academia – becomes unpalatable; often such choices lead to adding significant cost and schedule risk to projects seeking to minimize surprises during integration.  In addition, this model provides no mechanism to facilitate backwards compatibility between new and old components.  The Department of Defense (DoD) and particularly the Department of the Navy (DoN) have chosen to standardize on Red Hat Enterprise Linux and its close variants, where feasible.  This works reports on applied research at NUWC Newport that has fielded development environments based on Red Hat Enterprise Linux 6 to users and compatible Community Enterprise Operating System (CentOS) 6 environments to maritime robotic platforms.  We present our lessons learned from making the switch, discuss the methodology for correctly backporting required packages to get MOOS-IvP and Gobysoft applications up-and-running correctly, and future work focused on increasing backwards compatibility amongst our systems.
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!!!! %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.
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* Vehicle Safety
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!!!! %color=#7777BB% [[Talk.01-LePage | Prev-Talk]]%%  | \
%color=#7777BB%[[Talk.02-Schmidt | Next-Talk]]%% | \
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!!!! %color=#7777BB% [[Talk.01-Schneider | Prev-Talk]]%%  | \
%color=#7777BB%[[Talk.02-Purvis | Next-Talk]]%% | \
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* Anti-Submarine Warfare
*
Autonomy / Collaborative Autonomy
* The Ocean Explorer UUV
* Unmanned Underwater Vehicles (UUVs) / Autonomous Underwater Vehicles (AUVs)
* MOOS-IvP
* IvP Helm Behavior Development
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.

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* Autonomy / Collaborative Autonomy
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* 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
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!!!!%color=#449944% '''Kevin LePage, NATO Undersea Research Centre'''
to:
!!!!%color=#449944% '''Kevin LePage, NATO Undersea Research Centre (NURC)'''
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* UUVs/AUVs
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* MOOS-IvP
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* Ocean Explorer UUV
* 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
.



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

%%
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!! 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.




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* MOOS Core
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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
.


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
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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:
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%color=#7777BB%[[Talk.05-BillinGumstix|Next-Talk]]%% 
to:
%color=#7777BB%[[Talk.05-BillinGumstix|Next-Talk]]%% | \
%color=#7777BB%[[Talk.Listing|All-Talks
]]%% 
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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'''
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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]] 
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%color=#7777BB%[[Talk.05-BillinGumstix|Next-Talk]]%% 
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!!!! %color=#444499% [[Talk.03-Redfield|Prev-Talk]]%%  | [[Talk.05-BillinGumstix|Next-Talk]] 
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!!!! %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]] 
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!!!! %color=#444499% [[Talk.03-Redfield|Prev-Talk]]%%  | [[Talk.05-BillinGumstix|Next-Talk]] 
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[[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]] 
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[[Talk.04-Redfield|Prev-Talk]]  | [[Talk.04-Redfield|Next-Talk]] 
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[[Talk.03-Redfield|Prev-Talk]]  | [[Talk.05-BillinGumstix|Next-Talk]] 
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[[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'''
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!! ''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:''' \
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* Navy Labs
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!! ''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.


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* Multi-Vehicle Autonomy
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* Neutralization
to:
* MCM
Deleted line 18:
* USVs
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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 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.
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* Environmental Sampling
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* Neutralization
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* USVs
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* 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%%
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
Changed line 7 from:
%color=#449944% '''Andrew Bouchard, NSWC PCD'''
to:
%color=#449944% '''Andrew Bouchard, NSWC-PCD'''
Changed line 7 from:
%color=#449944% '''Andrew Bouchard, NSWC PCD'''%
to:
%color=#449944% '''Andrew Bouchard, NSWC PCD'''
Changed line 7 from:
%color=#449944% !!! '''Andrew Bouchard, NSWC PCD'''%
to:
%color=#449944% '''Andrew Bouchard, NSWC PCD'''%
Changed line 7 from:
!!! '''Andrew Bouchard, NSWC PCD'''
to:
%color=#449944% !!! '''Andrew Bouchard, NSWC PCD'''%
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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:
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'''
Changed lines 5-6 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.
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.