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AUTONOMY

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AUTONOMY

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  1. AUTONOMY MIT Graduate Student Open House March 24, 2000

  2. Deep Space 2 Failure Reason: ??? courtesy of NASA JPL Motivation • Failures • Anomalies • Communication • Commanding Mars Observer Pressurized leak of both helium gas and liquid MMH Mars Climate Orbiter Navigation error courtesy of NASA JPL • Apollo 13 • Quintuple fault identified (three shorts, tank-line and pressure jacket burst, panel flies off). courtesy of NASA courtesy of NASA JPL Mars Polar Lander Failure Reason: ??? courtesy of NASA JPL

  3. Reconfiguring hardware Recovering from faults Avoiding failures Coordinating control policies Monitoring Confirming commands Tracking goals Detecting anomalies Diagnosing faults Autonomy Planning & Execution Diagnosis Replanning Monitoring & Fault Detection Reconfiguration Coding Challenge Programmers and operators must reason through system-wide interactions: Identifying Modes Reconfiguring Modes

  4. Ground System Flight H/W Remote Agent Mission Manager Procedural Executive Real-Time Control RECOVERY EXECUTION Planner/ Scheduler MI & MR Monitors PLANNING Planning Experts State-of-the-Art & Future Started: January 1996 Launch: Oct 15th, 1998 • Self-commanding • Self-diagnosing • Self-repairing Self-modeling Collaborative Adaptive Anticipating Information-seeking

  5. Goal directed Closed-loop on goals Model-based programming Mission Goal Scenario Mission Manager Mission-level actions & resources Model-base Scripted Executive Fault Protection Planner/ Scheduler Real Time Software Projective Reactive Adaptive Control Real Time Software Hardware Autonomy Architecture • Highly deductive • Highly responsive • Model-based execution for fault protection

  6. Reactive Model-based Programming Language (RMPL) Program by specifying commonsense, compositional models. Have engineers program in models, automate synthesis of code: Models are compositional and highly reusable Generative approach covers broad set of behaviors Commonsense models are easy to articulate at concept stage and insensitive to design variations Valve Stuck open 0.01 Open 0. 01 Open Close 0. 01 Stuck closed Closed 0.01 inflow = outflow = 0 Valve Driver .1 sec 5 W .1 sec 5 W Resettable failure Reset On 0.01 Turn on Turn off Turn off .1 sec 5 W .1 sec 0 W 0.001 Permanent failure Off .1 sec 0 W Model-based Programming Programmers generate breadth of functions from commonsense models in light of mission goals.

  7. Concurrent Transition Systems Declarative, probabilistic, temporal delay, partial observability, indirect control, operating procedures… Distributed Hardware Concurrent State Machines Software Hierarchical Automata Uncertainty and Anomalies Hidden Markov Models Continuous Processes Qualitative Algebra e d d e c _ d Unified Model • Hierarchical, Probabilistic Constraint Automata (HCA) • All RMPL models are reduced to Hierarchical, Probabilistic Constraint Automata • Used in planning, model-based diagnosis, learning, scheduling, qualitative reasoning, execution... RMPL Models HCA Model

  8. Inputs: RMPL models of vehicles and hierarchical descriptions of activities, compiled down to HCA Mission-specific objectives and constraints Planner: Searches through projected state-space Extracts a set of consistent trajectories that achieve objectives Outputs: Temporally flexible plan that maximizes expected reward HCA Objectives & constraints Planner/ Scheduler e d d e c Plan _ d Model-based Planning

  9. Model-based, Stochastic, Optimal Controller Treats traditional flight software as a control system Maximizes likelihood and expected reward Observes and controls through logical constraints Scripted Executive Possible Modes Configuration Goals Command Model Mode Identification Mode Reconfiguration Reactive Planner current state goal state Observation Command Model-based Executive • Reason through system interactions on the fly, performing significant search & deduction within the reactive control loop • Conflict-directed • Best-first • Deductive • Reactive

  10. Hybrid Systems • Combination of discrete/continuous time system modeling • Discrete: constraint-based propositional logic • Continuous: system dynamics using PDE’s and non-linear elements • Autonomous fault/anomaly identification and resolution • Monitor model outputs and compare to measured states • Identify differences between model and measured states • Localize the origin of the differences • Suggest adaptations to model to reflect fault/mutation states • Our uses of Hybrid systems • Represent complicated systems with accurate hybrid models • Autonomous localization of the source of differences between models and reality during mission phase • Optimize model-based control system to incorporate changes in models during mission phase

  11. ISS Testbeds • Portable Satellite Assistant (PSA) • Indoor operation on ISS • Complete Autonomous operation • Issues • Astronaut/Robot interaction • Multiple PSA coordination • Tasks • Environmental monitoring • Virtual presence for science PI’s • Bioregenerative Planetary Life Support System Test Complex (BIOPLEX) • Life-support testbed for future Mars Missions • Complete Autonomous operation • Test advanced AI techniques before a mission

  12. Distributed Systems Testbeds • Coordinated Distributed • Planning and Execution • Limited communication • Unexpected events • Failure and anomalies • Multiple Air Vehicle Missions • Mars airplane • Unmanned Combat Air Vehicle (UCAV) • Distributed Spacecraft • TechSat21 simulation on GFLOPS • SPHERES formation flying testbed

  13. Future Autonomous Vehicles Space Technology 3 Europa Hydrobot In-Situ Propellant Plant Rovers X-34 RLV Prototype Mars Life Support Facility