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Premium member Presentation Transcript Autonomy Architectures for a Constellation of Spacecraft: Autonomy Architectures for a Constellation of Spacecraft Anthony Barrett Jet Propulsion Laboratory California Institute of Technology Presented by Russell KnightMotivation: Objective To control fleets/constellations of spacecraft with collective mission goals Why? Many proposals for such missions have recently appeared. Interferometers Plasma Physics Robotic colonies MotivationOutline: Outline The 4 Components of Autonomy Architecture 1: Master/Slave Coordination Architecture 2: Teamwork Architecture 3: Peer-to-Peer Coordination Concluding RemarksScientist to SpacecraftConnectivity: Scientist to Spacecraft Connectivity Objective Let scientists directly command their spacecraft over the internet Motivations Lets scientists command spacecraft through internet. Gives scientists feedback by rapidly checking if a science plan is feasible. Facilitates migrating the process onto the spacecraftScientist to SpacecraftConnectivity: ASPEN/WITS Scientists generate a set of requests in WITS, and an automated path planner generates the path. Planner/Scheduler automatically validates mission plan against flight rules, and inserts maintenance activities (human intervention through GUI also allowed). Executive & Diagnostician macro expands activities into executable rover actions, feeding them to the rover’s reactive control, and interpreting the results. Scientist to Spacecraft ConnectivityTechnological Progression: Technological ProgressionAutonomy for One: Autonomy for One Description To migrate planning, scheduling, robust execution, and diagnostics onto a spacecraft. Motivation Faster: makes replanning more responsive to anomalies Better: facilitates missions to poorly understood environments Cheaper: reduces operations costs and increases efficient use of limited communicationsSlide8: Deep Space 1 Remote Agent ExperimentSlide9: CASPER Continuous Planner ArchitectureTightly Coordinated Autonomy: Tightly Coordinated Autonomy Description To command a group of spacecraft where all spacecraft are tightly coordinated to achieve joint goals. Motivation To make missions with multiple tightly coupled spacecraft faster, better, and cheaper To enable missions with more spacecraft by reducing the communications per spacecraft requirements.Autonomous Agent Architecture(Master/Slave): Autonomous Agent Architecture (Master/Slave)Three Spacecraft Interferometer: The combiner S/C is the master and the collector S/C are the slaves. Each slave iteratively collects its sensor values into a packet, transmits the packet to the master, receives a control packet from the master, and distributes the packet’s bits to its actuators. Observation bandwidth  (# sensor bits)/(feedback latency) This approach does not scale well with the number of slaves or their complexity. Three Spacecraft InterferometerAutonomous Agent Architecture(Teamwork): Autonomous Agent Architecture (Teamwork) Actuator Signals & Team State Changes Sensor feedback & Team State Changes Data Objectives Actuator Signals & Team State Changes Goals, Sensor feedback & Team State ChangesSlide14: Coordination for Multiple Spacecraft Benefits Teamwork localizes feedback loops to reduces bandwidth. Distributed operations utilizes parallel computing resources for diagnostics.Loosely Coordinated Autonomy: Loosely Coordinated Autonomy Description To command a constellation where each spacecraft operates independently after getting its goals. Motivation To make missions with multiple spatially-isolated spacecraft faster, better, and cheaper To facilitate task migration between spacecraft as remote environmental conditions vary.Autonomous Agent Architecture(Peer-to-Peer): Autonomous Agent Architecture (Peer-to-Peer)Loosely Coordinated Constellation Example: Loosely Coordinated Constellation ExampleConcluding Remarks: Concluding Remarks This talk described a 4 module anatomy of an autonomous spacecraft’s software and showed how to define 3 different constellation autonomy architectures by placing modules on spacecraft. These architectures are orthogonal. A constellation can have several peers that lead a set of followers, and they each can teleoperate several slaves.Contact Information: Contact Information Anthony Barrett Artificial Intelligence Group Jet Propulsion Laboratory California Institute of Technology anthony.barrett@jpl.nasa.gov http://www-aig.jpl.nasa.gov/ You do not have the permission to view this presentation. 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isairas99 Vincenza Download Post to : URL : Related Presentations : Share Add to Flag Embed Email Send to Blogs and Networks Add to Channel Uploaded from authorPOINTLite Insert YouTube videos in PowerPont slides with aS Desktop Copy embed code: Embed: Flash iPad Dynamic Copy Does not support media & animations Automatically changes to Flash or non-Flash embed WordPress Embed Customize Embed URL: Copy Thumbnail: Copy The presentation is successfully added In Your Favorites. Views: 57 Category: Education License: All Rights Reserved Like it (0) Dislike it (0) Added: January 22, 2008 This Presentation is Public Favorites: 0 Presentation Description No description available. Comments Posting comment... Premium member Presentation Transcript Autonomy Architectures for a Constellation of Spacecraft: Autonomy Architectures for a Constellation of Spacecraft Anthony Barrett Jet Propulsion Laboratory California Institute of Technology Presented by Russell KnightMotivation: Objective To control fleets/constellations of spacecraft with collective mission goals Why? Many proposals for such missions have recently appeared. Interferometers Plasma Physics Robotic colonies MotivationOutline: Outline The 4 Components of Autonomy Architecture 1: Master/Slave Coordination Architecture 2: Teamwork Architecture 3: Peer-to-Peer Coordination Concluding RemarksScientist to SpacecraftConnectivity: Scientist to Spacecraft Connectivity Objective Let scientists directly command their spacecraft over the internet Motivations Lets scientists command spacecraft through internet. Gives scientists feedback by rapidly checking if a science plan is feasible. Facilitates migrating the process onto the spacecraftScientist to SpacecraftConnectivity: ASPEN/WITS Scientists generate a set of requests in WITS, and an automated path planner generates the path. Planner/Scheduler automatically validates mission plan against flight rules, and inserts maintenance activities (human intervention through GUI also allowed). Executive & Diagnostician macro expands activities into executable rover actions, feeding them to the rover’s reactive control, and interpreting the results. Scientist to Spacecraft ConnectivityTechnological Progression: Technological ProgressionAutonomy for One: Autonomy for One Description To migrate planning, scheduling, robust execution, and diagnostics onto a spacecraft. Motivation Faster: makes replanning more responsive to anomalies Better: facilitates missions to poorly understood environments Cheaper: reduces operations costs and increases efficient use of limited communicationsSlide8: Deep Space 1 Remote Agent ExperimentSlide9: CASPER Continuous Planner ArchitectureTightly Coordinated Autonomy: Tightly Coordinated Autonomy Description To command a group of spacecraft where all spacecraft are tightly coordinated to achieve joint goals. Motivation To make missions with multiple tightly coupled spacecraft faster, better, and cheaper To enable missions with more spacecraft by reducing the communications per spacecraft requirements.Autonomous Agent Architecture(Master/Slave): Autonomous Agent Architecture (Master/Slave)Three Spacecraft Interferometer: The combiner S/C is the master and the collector S/C are the slaves. Each slave iteratively collects its sensor values into a packet, transmits the packet to the master, receives a control packet from the master, and distributes the packet’s bits to its actuators. Observation bandwidth  (# sensor bits)/(feedback latency) This approach does not scale well with the number of slaves or their complexity. Three Spacecraft InterferometerAutonomous Agent Architecture(Teamwork): Autonomous Agent Architecture (Teamwork) Actuator Signals & Team State Changes Sensor feedback & Team State Changes Data Objectives Actuator Signals & Team State Changes Goals, Sensor feedback & Team State ChangesSlide14: Coordination for Multiple Spacecraft Benefits Teamwork localizes feedback loops to reduces bandwidth. Distributed operations utilizes parallel computing resources for diagnostics.Loosely Coordinated Autonomy: Loosely Coordinated Autonomy Description To command a constellation where each spacecraft operates independently after getting its goals. Motivation To make missions with multiple spatially-isolated spacecraft faster, better, and cheaper To facilitate task migration between spacecraft as remote environmental conditions vary.Autonomous Agent Architecture(Peer-to-Peer): Autonomous Agent Architecture (Peer-to-Peer)Loosely Coordinated Constellation Example: Loosely Coordinated Constellation ExampleConcluding Remarks: Concluding Remarks This talk described a 4 module anatomy of an autonomous spacecraft’s software and showed how to define 3 different constellation autonomy architectures by placing modules on spacecraft. These architectures are orthogonal. A constellation can have several peers that lead a set of followers, and they each can teleoperate several slaves.Contact Information: Contact Information Anthony Barrett Artificial Intelligence Group Jet Propulsion Laboratory California Institute of Technology anthony.barrett@jpl.nasa.gov http://www-aig.jpl.nasa.gov/