01-Manda
Talk.01-Manda History
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The hardware for the system is designed to be housed in a single watertight box. Onboard processing has been deployed on the low cost BeagleBone Black and Raspberry Pi 2 platforms. Low level sensor input and control output as well as fail-safes and human remote control are handled by an independent Arduino microcontroller. Position and orientation input can be accepted from an existing source on the vessel or use a MEMS INS for simple deployments. The autonomy system has been implemented on multiple small vessels including those with both gas and electric engines.
The hardware for the system is designed to be housed in a single watertight box. Onboard processing has been deployed on the low cost BeagleBone Black and Raspberry Pi 2 platforms. Low level sensor input and control output as well as fail-safes and human remote control are handled by an independent Arduino microcontroller. Position and orientation input can be accepted from an existing source on the vessel or use a MEMS INS for simple deployments. The autonomy system has been implemented on multiple small vessels including those with both gas and electric engines. DOWNLOAD
Damian Manda, Andrew D’Amore, May-Win Thein, Andrew Armstrong (UNH)
Damian Manda, NOAA Office of Coast Survey, NOAA-UNH Joint Hydrographic Center, Ocean Engineering Program, University of New Hampshire
Andrew D’Amore, Systems Engineering Program, University of New Hampshire
May-Win Thein, Ocean Engineering Program, University of New Hampshire
Andrew Armstrong, NOAA Office of Coast Survey, NOAA-UNH Joint Hydrographic Center
- Payload Autonomy Interface
- Payload Autonomy Platform/Interface
- UUVs
- USVs
- Cross Domain UxVs
- Command and Control, Mission Planning
Talk-01: iFrontSeat: A New Approach for Writing Extensible MOOS-IvP Frontseat-Backseat Payload Interface Drivers
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.
Talk-01: A Flexible, Low-Cost MOOS-IvP Based Platform for Marine Autonomy Research
Damian Manda, Andrew D’Amore, May-Win Thein, Andrew Armstrong (UNH)
As part of an effort to research collaborative autonomy behaviors between multiple unmanned surface and underwater vehicles, a hardware and software system has been developed for control of autonomous surface vessels (ASVs). This system is designed to be flexible in application to diverse platforms and ability to execute complex missions. In order to facilitate duplication across many deployments, the cost of the full system is minimized by leveraging mass produced, open source hardware and software.
MOOS-IvP is used as the central data assimilation and decision making software. Stock supplied and newly developed IvP behaviors are used to plan the trajectory for the ASV and can be customized to suit platform requirements. A graphical interface is available for setting missing configuration parameters to simplify deployment by those not fully versed in MOOS mission file creation. Sensor data is assimilated through either MOOS interface drivers or using ROS software and then passed to MOOS for use by the IvP helm and navigation controller. Incorporating ROS allows flexibility in sensor selection as many drivers already exist in the community and can be quickly adapted to this autonomy system.
The hardware for the system is designed to be housed in a single watertight box. Onboard processing has been deployed on the low cost BeagleBone Black and Raspberry Pi 2 platforms. Low level sensor input and control output as well as fail-safes and human remote control are handled by an independent Arduino microcontroller. Position and orientation input can be accepted from an existing source on the vessel or use a MEMS INS for simple deployments. The autonomy system has been implemented on multiple small vessels including those with both gas and electric engines.
- Vehicle Safety
- Bluefin Robotics
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.
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.
- Anti-Submarine Warfare
- Autonomy / Collaborative Autonomy
- The Ocean Explorer UUV
- Unmanned Underwater Vehicles (UUVs) / Autonomous Underwater Vehicles (AUVs)
- MOOS-IvP
- IvP Helm Behavior Development
- Payload Autonomy Interface
- Bluefin Robotics
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
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.
Talk-01: iFrontSeat: A New Approach for Writing Extensible MOOS-IvP Frontseat-Backseat Payload Interface Drivers
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.
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.
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.
- Ocean Explorer UUV
- UUVs/AUVs
- The Ocean Explorer UUV
- Unmanned Underwater Vehicles (UUVs) / Autonomous Underwater Vehicles (AUVs)
- New IvP Helm Behavior
- IvP Helm Behavior Development
Kevin LePage, NATO Undersea Research Centre
Kevin LePage, NATO Undersea Research Centre (NURC)
- MOOS-IvP
- Ocean Explorer UUV
- MOOS-IvP
- New IvP Helm Behavior
Talk-01: MOOS Then, Now and Next
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.
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
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.
- MOOS Core
- Academia
- MOOS-IvP
- Anti-Submarine Warfare
Talk-01: MOOS Updates (PLACEHOLDER)
Talk-01: MOOS Then, Now and Next
No Abstract Yet.
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.
- MOOS Core
Talk-29: Integration and Testing of a Novel Reacquire/Identify Pattern Generation Algorithm
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.
Talk-01: MOOS Updates (PLACEHOLDER)
Paul Newman, Oxford
No Abstract Yet.
- Autonomy
- MOOS-IvP
- MCM
- UUVs
- Navy Labs
- Academia
Talk-04: Integration and Testing of a Novel Reacquire/Identify Pattern Generation Algorithm
Talk-29: Integration and Testing of a Novel Reacquire/Identify Pattern Generation Algorithm
Matthew J. Bays, Jean- François Kamath and Signe A. Redfield, NSWC-PCD
Matthew J. Bays, Jean-François Kamath and Signe A. Redfield, NSWC-PCD
Matthew J. Bays, Jean- François Kamath and Signe A. Redfield, NSWC-PCD
Matthew J. Bays, Jean- François Kamath and Signe A. Redfield, NSWC-PCD
Integration and Testing of a Novel Reacquire/Identify Pattern Generation Algorithm
Talk-04: Integration and Testing of a Novel Reacquire/Identify Pattern Generation Algorithm
MOOS-Enabled Semi-Autonomous Remote USV Operations
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.
Integration and Testing of a Novel Reacquire/Identify Pattern Generation Algorithm
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.
- Multi-Vehicle Autonomy
- Neutralization
- MCM
- USVs
Autonomous Adaptive Environmental Feature Tracking on Board AUVs: Tracking the Thermocline
Stephanie Petillo, MIT (LAMSS)
This talk addresses the challenge of autonomously and adaptively tracking features of the underwater environment using AUVs running the MOOS-IvP autonomy software. This problem is addressed from concept to implementation in the field on various AUV platforms, developing specifically the example of thermocline tracking. Some recent research involving methods for feature tracking on board multiple AUVs operating simultaneously and collaboratively to detect an underwater feature will also be discussed briefly.
MOOS-Enabled Semi-Autonomous Remote USV Operations
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.
- Environmental Sampling
- Neutralization
- USVs
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.
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.
- Acoustic Communications,
- Environmental Sampling
- Autonomy
- Autonomy
- MOOS-IvP
'Stephanie Petillo, MIT (LAMSS)
Stephanie Petillo, MIT (LAMSS)
Unmanned Robot Message Optimization Method (URMOM)
Andrew Bouchard, NSWC-PCD
Autonomous Adaptive Environmental Feature Tracking on Board AUVs: Tracking the Thermocline
'Stephanie Petillo, MIT (LAMSS)
Topics: Acoustic Communications, Multi-Vehicle Autonomy, Autonomy
Topics:
- Acoustic Communications,
- Multi-Vehicle Autonomy
- Autonomy
Topics: Acoustic Communications, Multi-Vehicle Autonomy, Autonomy
Topics: Acoustic Communications, Multi-Vehicle Autonomy, Autonomy
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.
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
Andrew Bouchard, NSWC PCD
Andrew Bouchard, NSWC-PCD
Andrew Bouchard, NSWC PCD%
Andrew Bouchard, NSWC PCD
!!! Andrew Bouchard, NSWC PCD%
Andrew Bouchard, NSWC PCD%
Andrew Bouchard, NSWC PCD
!!! Andrew Bouchard, NSWC PCD%
Andrew Bouchard, NSWC PCD
Andrew Bouchard, NSWC PCD
Title: Unmanned Robot Message Optimization Method (URMOM)
Unmanned Robot Message Optimization Method (URMOM)
Title: Unmanned Robot Message Optimization Method (URMOM)
Title: Unmanned Robot Message Optimization Method (URMOM)
Title: Unmanned Robot Message Optimization Method (URMOM)
Andrew Bouchard, NSWC PCD
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Title: Unmanned Robot Message Optimization Method (URMOM)
Andrew Bouchard, NSWC PCD
One of the greatest challenges of working in the underwater regime is the
severe limitations of acoustic communications. This problem becomes even more evident in multi-vehicle autonomy, when vehicles must continually update each other with their state and intentions to achieve cooperative goals. In order to support tests of a multi-vehicle arbiter framework, an optimization scheme was created and implemented as a MOOS module to enable sufficient message passing between vehicles. Using this tool, vehicle state and destination, shared map updates, updated algorithm parameters, target information, and decision reconciliation can be effectively shared between vehicles using the published Compact Control Language (CCL) standard for acoustic messages.
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.
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.
One of the greatest challenges of working in the underwater regime is the
severe limitations of acoustic communications. This problem becomes even more evident in multi-vehicle autonomy, when vehicles must continually update each other with their state and intentions to achieve cooperative goals. In order to support tests of a multi-vehicle arbiter framework, an optimization scheme was created and implemented as a MOOS module to enable sufficient message passing between vehicles. Using this tool, vehicle state and destination, shared map updates, updated algorithm parameters, target information, and decision reconciliation can be effectively shared between vehicles using the published Compact Control Language (CCL) standard for acoustic messages.
Title: Unmanned Robot Message Optimization Method (URMOM)
Andrew Bouchard, NSWC PCD
One of the greatest challenges of working in the underwater regime is the severe limitations of acoustic communications. This problem becomes even more evident in multi-vehicle autonomy, when vehicles must continually update each other with their state and intentions to achieve cooperative goals. In order to support tests of a multi-vehicle arbiter framework, an optimization scheme was created and implemented as a MOOS module to enable sufficient message passing between vehicles. Using this tool, vehicle state and destination, shared map updates, updated algorithm parameters, target information, and decision reconciliation can be effectively shared between vehicles using the published Compact Control Language (CCL) standard for acoustic messages.