Space Systems Laboratory Massachusetts Institute of Technology AUTONOMY MIT Graduate Student Open House March 24, 2000

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

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

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

Space Systems Laboratory Massachusetts Institute of Technology Autonomy Architecture Goal directed Closed-loop on goals Model-based programming Real Time Software Hardware Adaptive Control Planner/ Scheduler Scripted Executive Fault Protection Mission Manager Real Time Software Mission Goal Scenario Model-base Projectiv e Reactive Mission- level actions & resources Highly deductive Highly responsive Model-based execution for fault protection

Space Systems Laboratory Massachusetts Institute of Technology Model-based Programming 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 Programmers generate breadth of functions from commonsense models in light of mission goals. ClosedValveOpen Stuckopen Stuckclosed OpenClose inflow = outflow = 0 Valve Driver Turn on Turnoff Turn off Reset On Off Resettablefailure Permanentfailure.1 sec 5 W sec 0 W.1 sec 5 W.1 sec 5 W.1 sec 0 W

Space Systems Laboratory Massachusetts Institute of Technology Unified Model Concurrent Transition Systems –Declarative, probabilistic, temporal delay, partial observability, indirect control, operating procedures… 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... Distributed HardwareConcurrent State Machines SoftwareHierarchical Automata Uncertainty and AnomaliesHidden Markov Models Continuous ProcessesQualitative Algebra c e e dd d _ Model RMPL ModelsHCA

Space Systems Laboratory Massachusetts Institute of Technology Model-based Planning 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 Planner/ Scheduler Planner/ Scheduler HCA c e e dd d _ Plan Objectives & constraints Objectives & constraints

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

Space Systems Laboratory Massachusetts Institute of Technology 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

Space Systems Laboratory Massachusetts Institute of Technology ISS Testbeds 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 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

Space Systems Laboratory Massachusetts Institute of Technology Distributed Systems Testbeds Multiple Air Vehicle Missions Mars airplane Unmanned Combat Air Vehicle (UCAV) Coordinated Distributed Planning and Execution Limited communication Unexpected events Failure and anomalies Distributed Spacecraft TechSat21 simulation on GFLOPS SPHERES formation flying testbed

Space Systems Laboratory Massachusetts Institute of Technology Future Autonomous Vehicles Space Technology 3 Mars Life Support Facility Rovers Europa HydrobotIn-Situ Propellant Plant X-34 RLV Prototype