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Michael R. Benjamin - One of the best experts on this subject based on the ideXlab platform.
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Learning Autonomous Marine Behaviors in MOOs-IvP
OCEANS 2018 MTS IEEE Charleston, 2018Co-Authors: Arjun Gupta, Michael Novitzky, Michael R. BenjaminAbstract:Manually authoring and testing behaviors for autonomous marine vehicles can become tedious and impractical when faced with complex or rapidly changing adversarial situations. We address this problem by learning autonomous behaviors using deep reinforcement learning. We apply deep reinforcement learning, an approach that learns behaviors without relying on explicit vehicle models, to a game of capture the flag with multiple competing vehicles. We integrated deep reinforcement learning with MOOs-IvP, a software suite for marine robotics communication, control, and simulation, that allows the development and execution of behaviors for both underwater and surface vehicles. To our knowledge, this is the first application of reinforcement learning to this platform. We extended MOOs-IvP to create and train a neural net to learn autonomous behaviors for reaching the opponent’s flag while avoiding an adversary exhibiting a defense behavior in simulation.
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Autonomy for Unmanned Marine Vehicles with MOOs-IvP
Marine Robot Autonomy, 2012Co-Authors: Michael R. Benjamin, Henrik Schmidt, Paul Newman, John J. LeonardAbstract:This chapter describes the MOOs-IvP autonomy software for unmanned marine vehicles and its use in large-scale ocean sensing systems. MOOs-IvP is comprised of two open-source software projects. MOOs provides a core autonomy middleware capability and the MOOs project additionally provides a set of ubiquitous infrastructure utilities. The IvP Helm is the primary component of an additional set of capabilities implemented to form a full marine autonomy suite known as MOOs-IvP. This software and architecture are platform and mission agnostic and allow for a scalable nesting of unmanned vehicle nodes to form large-scale, long-endurance ocean sensing systems comprised of heterogeneous platform types with varying degrees of communications connectivity, bandwidth, and latency.
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MOOs ivp autonomy tools users manual release 4 2 1
2011Co-Authors: Michael R. BenjaminAbstract:This document describes 19 MOOs-IvP autonomy tools. uHelmScope provides a run-time scoping window into the state of an active IvP Helm executing its mission. pMarineViewer is a geo-based GUI tool for rendering marine vehicles and geometric data in their operational area. uXMS is a terminal based tool for scoping on a MOOsDB process. uTermCommand is a terminal based tool for poking a MOOsDB with a set of MOOs file pre-defined variable-value pairs selectable with aliases from the command-line. pEchoVar provides a way of echoing a post to one MOOs variable with a new post having the same value to a different variable. uProcessWatch monitors the presence or absence of a set of MOOs processes and summarizes the collective status in a single MOOs variable. uPokeDB provides a way of poking the MOOsDB from the command line with one or more variable-value pairs without any pre-existing configuration of a MOOs file. uTimerScript will execute a pre-defined timed pausable script of poking variable-value pairs to a MOOsDB. pNodeReporter summarizes a platforms critical information into a single node report string for sharing beyond the vehicle. pBasicContactMgr provides a basic contact management service with the ability to generate range-dependent configurable alerts. uSimMarine provides a simple marine vehicle simulator. uSimBeaconRange and uSimContactRange provide further simulation for range-only sensors. The Alog Toolbox is a set of offline tools for analyzing and manipulating log files in the .alog format. This work is the product of a multi-year collaboration between the Department of Mechanical Engineering and the Computer Science and Artificial Intelligence Laboratory (CSAIL) at the Massachusetts Institute of Technology in Cambridge Massachusetts, and the Oxford University Mobile Robotics Group. Points of contact for collaborators: Dr. Michael R. Benjamin Department of Mechanical Engineering Computer Science and Artificial Intelligence Laboratory Massachusetts Intitute of Technology mikerb@csail.mit.edu Prof. John J. Leonard Department of Mechanical Engineering Computer Science and Artificial Intelligence Laboratory Massachusetts Intitute of Technology jleonard@csail.mit.edu Prof. Henrik Schmidt Department of Mechanical Engineering Massachusetts Intitute of Technology henrik@mit.edu Dr. Paul Newman Department of Engineering Science University of Oxford pnewman@robots.ox.ac.uk Other collaborators have contributed greatly to the development and testing of software and ideas within, notably Joseph Curcio, Toby Schneider, Stephanie Kemna, Arjan Vermeij, Don Eickstedt, Andrew Patrikilakis, Arjuna Balasuriya, David Battle, Christian Convey, Chris Gagner, Andrew Shafer, and Kevin Cockrell. Sponsorship, and public release information: This work is sponsored by Dr. Behzad Kamgar-Parsi and Dr. Don Wagner of the Office of Naval Research (ONR), Code 311. Further support for testing and coursework development sponsored by Battelle, Dr. Robert Carnes.
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Nested Autonomy for Unmanned Marine Vehicles with MOOs-IvP
Journal of Field Robotics, 2010Co-Authors: Michael R. Benjamin, Henrik Schmidt, Paul Newman, John J. LeonardAbstract:This document describes the MOOs-IvP autonomy software for unmanned marine vehicles and its use in large-scale ocean sensing systems. MOOs-IvP is composed of two open-source software projects funded by the Office of Naval Research. MOOs provides a core autonomy middleware capability, and the MOOs project additionally provides a set of ubiquitous infrastructure utilities. The IvP Helm is the primary component of an additional set of capabilities implemented to form a full marine autonomy suite known as MOOs-IvP. This software and architecture are platform and mission agnostic and allow for a scalable nesting of unmanned vehicle nodes to form large-scale, long-endurance ocean sensing systems composed of heterogeneous platform types with varying degrees of communications connectivity, bandwidth, and latency. © 2010 Wiley Periodicals, Inc.
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MOOs-IvP Autonomy Tools Users Manual
2010Co-Authors: Michael R. BenjaminAbstract:This document describes fifteen MOOs-IvP autonomy tools. uHelmScope provides a runtime scoping window into the state of an active IvP Helm executing its mission. pMarineViewer is a geo-based GUI tool for rendering marine vehicles and geometric data in their operational area. uXMS is a terminal based tool for scoping on a MOOsDB process. uTermCommand is a terminal based tool for poking a MOOsDB with a set of MOOs file pre-defined variable-value pairs selectable with aliases from the command-line. pEchoVar provides a way of echoing a post to one MOOs variable with a new post having the same value to a different variable. uProcessWatch monitors the presence or absence of a set of MOOs processes and summarizes the collective status in a single MOOs variable. uPokeDB provides a way of poking the MOOsDB from the command line with one or more variable-value pairs without any pre-existing configuration of a MOOs file. uTimerScript will execute a pre-defined timed pausable script of poking variable-value pairs to a MOOsDB. pNodeReporter summarizes a platforms critical information into a single node report string for sharing beyond the vehicle. pBasicContactMgr provides a basic contact management service with the ability to generate range-dependent configurable alerts. The Alog Toolbox is a set of offline tools for analyzing and manipulating log files in the .alog format. Approved for public release; Distribution is unlimited. This work is the product of a multi-year collaboration between the Center for Advanced System Technologies (CAST), Code 2501, of the Naval Undersea Warfare Center in Newport Rhode Island and the Department of Mechanical Engineering and the Computer Science and Artificial Intelligence Laboratory (CSAIL) at the Massachusetts Institute of Technology in Cambridge Massachusetts, and the Oxford University Mobile Robotics Group. Points of contact for collaborators: Dr. Michael R. Benjamin Center for Advanced System Technologies NUWC Division Newport Rhode Island Michael.R.Benjamin@navy.mil mikerb@csail.mit.edu Prof. John J. Leonard Department of Mechanical Engineering Computer Science and Artificial Intelligence Laboratory Massachusetts Intitute of Technology jleonard@csail.mit.edu Prof. Henrik Schmidt Department of Mechanical Engineering Massachusetts Intitute of Technology henrik@mit.edu Dr. Paul Newman Department of Engineering Science University of Oxford pnewman@robots.ox.ac.uk Other collaborators have contributed greatly to the development and testing of software and ideas within, notably Joseph Curcio, Don Eickstedt, Andrew Patrikilakis, Toby Schneider, Arjuna Balasuriya, David Battle, Christian Convey, Chris Gagner, Andrew Shafer, and Kevin Cockrell. Sponsorship, and public release information: This work is sponsored by Dr. Behzad Kamgar-Parsi and Dr. Don Wagner of the Office of Naval Research (ONR), Code 311. Information on Navy public release approval for this document can be obtained from the Technical Library at the Naval Undersea Warfare Center, Division Newport RI.
Christian J Doonan - One of the best experts on this subject based on the ideXlab platform.
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application of metal and metal oxide nanoparticles mofs
Coordination Chemistry Reviews, 2016Co-Authors: Paolo Falcaro, Raffaele Ricco, Amirali Yazdi, Inhar Imaz, Shuhei Furukawa, Daniel Maspoch, Rob Ameloot, Jack D Evans, Christian J DoonanAbstract:Abstract Composites based on Metal-Organic Frameworks (MOFs) are an emerging class of porous materials that have been shown to possess unique functional properties. Nanoparticles@MOFs composites combine the tailorable porosity of MOFs with the versatile functionality of metal or metaloxide nanoparticles. A wide range of nanoparticles@MOFs have been synthesised and their performance characteristics assessed in molecular adsorption and separation, catalysis, sensing, optics, sequestration of pollutants, drug delivery, and renewable energy. This review covers the main research areas where nanoparticles@MOFs have been strategically applied and highlights the scientific challenges to be considered for their continuing development.
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electronic structure description of the cis MOOs unit in models for molybdenum hydroxylases
Journal of the American Chemical Society, 2008Co-Authors: Christian J Doonan, Nick D Rubie, Katrina Peariso, Hugh H Harris, Sushilla Z Knottenbelt, Graham N George, Charles G Young, Martin L KirkAbstract:The molybdenum hydroxylases catalyze the oxidation of numerous aromatic heterocycles and simple organics and, unlike other hydroxylases, utilize water as the source of oxygen incorporated into the product. The electronic structures of the cis-MOOs units in CoCp2[TpiPrMoVOS(OPh)] and TpiPrMoVIOS(OPh) (TpiPr = hydrotris(3-isopropylpyrazol-1-yl)borate), new models for molybdenum hydroxylases, have been studied in detail using S K-edge X-ray absorption spectroscopy, vibrational spectroscopy, and detailed bonding calculations. The results show a highly delocalized MoS π* LUMO redox orbital that is formally Mo(dxy) with ∼35% sulfido ligand character. Vibrational spectroscopy has been used to quantitate Mo−Ssulfido bond order changes in the cis-MOOs units as a function of redox state. Results support a redox active molecular orbital that has a profound influence on MOOs bonding through changes to the relative electro/nucleophilicity of the terminal sulfido ligand accompanying oxidation state changes. The bonding...
John J. Leonard - One of the best experts on this subject based on the ideXlab platform.
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Autonomy for Unmanned Marine Vehicles with MOOs-IvP
Marine Robot Autonomy, 2012Co-Authors: Michael R. Benjamin, Henrik Schmidt, Paul Newman, John J. LeonardAbstract:This chapter describes the MOOs-IvP autonomy software for unmanned marine vehicles and its use in large-scale ocean sensing systems. MOOs-IvP is comprised of two open-source software projects. MOOs provides a core autonomy middleware capability and the MOOs project additionally provides a set of ubiquitous infrastructure utilities. The IvP Helm is the primary component of an additional set of capabilities implemented to form a full marine autonomy suite known as MOOs-IvP. This software and architecture are platform and mission agnostic and allow for a scalable nesting of unmanned vehicle nodes to form large-scale, long-endurance ocean sensing systems comprised of heterogeneous platform types with varying degrees of communications connectivity, bandwidth, and latency.
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Nested Autonomy for Unmanned Marine Vehicles with MOOs-IvP
Journal of Field Robotics, 2010Co-Authors: Michael R. Benjamin, Henrik Schmidt, Paul Newman, John J. LeonardAbstract:This document describes the MOOs-IvP autonomy software for unmanned marine vehicles and its use in large-scale ocean sensing systems. MOOs-IvP is composed of two open-source software projects funded by the Office of Naval Research. MOOs provides a core autonomy middleware capability, and the MOOs project additionally provides a set of ubiquitous infrastructure utilities. The IvP Helm is the primary component of an additional set of capabilities implemented to form a full marine autonomy suite known as MOOs-IvP. This software and architecture are platform and mission agnostic and allow for a scalable nesting of unmanned vehicle nodes to form large-scale, long-endurance ocean sensing systems composed of heterogeneous platform types with varying degrees of communications connectivity, bandwidth, and latency. © 2010 Wiley Periodicals, Inc.
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extending a MOOs ivp autonomy system and users guide to the ivpbuild toolbox
2009Co-Authors: Michael R. Benjamin, Henrik Schmidt, Paul Newman, John J. LeonardAbstract:This document describes how to extend the suite of MOOs applications and IvP Helm behaviors distributed with the MOOs-IvP software bundle from www.MOOs-ivp.org. It covers (a) a straw-man repository with a place-holder MOOs application and IvP Behavior, with a working CMake build structure, (b) a brief overview of the MOOs application class with an example application, and (c) an overview of the IvP Behavior class with an example behavior, and (d) the IvPBuild Toolbox for generation of objective functions within behaviors. Approved for public release; Distribution is unlimited. This work is the product of a multi-year collaboration between the Center for Advanced System Technologies (CAST), Code 2501, of the Naval Undersea Warfare Center in Newport Rhode Island and the Department of Mechanical Engineering and the Computer Science and Artificial Intelligence Laboratory (CSAIL) at the Massachusetts Institute of Technology in Cambridge Massachusetts, and the Oxford University Mobile Robotics Group. Points of contact for collaborators: Dr. Michael R. Benjamin Center for Advanced System Technologies NUWC Division Newport Rhode Island Michael.R.Benjamin@navy.mil mikerb@csail.mit.edu Prof. John J. Leonard Department of Mechanical Engineering Computer Science and Artificial Intelligence Laboratory Massachusetts Intitute of Technology jleonard@csail.mit.edu Prof. Henrik Schmidt Department of Mechanical Engineering Massachusetts Intitute of Technology henrik@mit.edu Dr. Paul Newman Department of Engineering Science University of Oxford pnewman@robots.ox.ac.uk Other collaborators have contributed greatly to the development and testing of software and ideas within, notably Joseph Curcio, Don Eickstedt, Andrew Patrikilakis, Toby Schneider, Arjuna Balasuriya, David Battle, Christian Convey, Andrew Shafer, and Kevin Cockrell. Sponsorship, and public release information: This work is sponsored by Dr. Behzad Kamgar-Parsi and Dr. Don Wagner of the Office of Naval Research (ONR), Code 311. Information on Navy public release approval for this document can be obtained from the Technical Library at the Naval Undersea Warfare Center, Division Newport RI.
Roger Collier - One of the best experts on this subject based on the ideXlab platform.
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MOOs your daddy
Canadian Medical Association Journal, 2012Co-Authors: Roger CollierAbstract:Though it is highly unlikely that a dog would ever be sued for child support, genetic testing for parentage is becoming altogether common in the canine population. “It’s like the human model of paternity testing, but for dogs,” says Randall Smith, account manager of the veterinary division of
Vladimir Djapic - One of the best experts on this subject based on the ideXlab platform.
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Command filtered backstepping design in MOOs-IvP helm framework for trajectory tracking of USVs
Proceedings of the 2010 American Control Conference, 2010Co-Authors: Vladimir DjapicAbstract:This article describes design and simulation implementation of a nonlinear controller for an underactuated surface vehicle. The controller is designed using a command filtered backstepping (CFBS) approach. Theoretical background for controller design is given in the first part of this article. This nonlinear controller can be used for accurate tracking of a complex trajectory, for example a circular trajectory. Second part of the article focuses on implementation in the MOOs-IvP framework. This framework allows for flexibility in control and mission planning. Guidance is covered by the MOOs-IvP implementation of the controller while the COTS autopilot handles low-level control. The control performance is verified in simulation which confirms arbitrarily small tracking error. This paper presents simulation results where external disturbances, such currents, are also simulated and compensated for.