Open House 05

Information about Open House 05

Published on November 6, 2007

Author: Alexan

Source: authorstream.com

Content

Autonomy:  Autonomy Model-based Embedded and Robotic Systems Group (MERS) Aero/Astro Graduate Open House March 18, 2005 The Need for Autonomy:  Mars Polar Lander failed due to a faulty sensor. Space systems must handle Faults and anomalies Cooperative exploration Long-term science operations Increasingly ambitious goals Earth Imager Europa Probe MER Memory Leak Mars Outpost The Need for Autonomy Apollo 13 quintuple fault Future Autonomous Vehicles:  Future Autonomous Vehicles Space Technology 3 Mars Life Support Facility 2009 Mars Science Lab Europa Hydrobot In-Situ Propellant Plant Orbital Space Plane Mission Collaboration:  Mission Collaboration Images courtesy of NASA JPL MER Shadow Mode MIT SPHERES Earth Observing-1 Orbital Express Mars Science Laboratory 2009 Robust Systems Should be “Fully State Aware”:  Robust Systems Should be “Fully State Aware” Embedded programs interact with plant sensors and actuators: Read sensors Set actuators Complexity: Programmer must map between state and sensors/actuators. Model-based programs interact with plant state: Read state Write state Simplification: Model-based executive maps between state and sensors/actuators. Embedded Program S Plant Observations Command Model-based Embedded Program S Plant Example: Orbital Insertion Scenario:  Engineers Think in Terms of State Evolution One of the two engines must be firing: Set both engines to standby. Prior to firing an engine, camera must be off. Once the camera is off and the primary engine is on standby, the primary engine should be fired. In case of primary engine failure, the backup engine should be fired instead. Example: Orbital Insertion Scenario Standby Engine Model Off off- cmd standby- cmd 0.01 (thrust = full) AND (power_in = nominal) Firing 0.01 standby- cmd fire- cmd (thrust = zero) AND (power_in = zero) (thrust = zero) AND (power_in = nominal) 0.01 Failed On Camera Model Off turnoff- cmd turnon- cmd (power_in = zero) AND (shutter = closed) (power_in = nominal) AND (shutter = open) EngineA EngineB Science Camera EngineA EngineB Science Camera Model-based Program:  Model-based Program Control program specifies state trajectories: Concurrency Preemption Queries hidden state Asserts (assigns) hidden state OrbitInsert():: (do-watching ((EngineA = Firing) OR (EngineB = Firing)) (parallel (EngineA = Standby) (EngineB = Standby) (Camera = Off) (do-watching (EngineA = Failed) (when-donext ( (EngineA = Standby) AND (Camera = Off) ) (EngineA = Firing))) (when-donext ( (EngineA = Failed) AND (EngineB = Standby) AND (Camera = Off) ) (EngineB = Firing)))) Plant Model describes behavior of each component: Nominal and off-nominal Qualitative constraints Likelihoods and costs Models are reusable and easy to articulate at the conceptual stage Titan Model-based Executive:  Titan Model-based Executive RMPL Model-based Executive Sequencer / Planner Generates target goal states conditioned on state estimates State goals State estimates Commands Observations Mode Estimation tracks likely plant state Mode Reconfiguration tracks least cost goal states Control program Plant Executes concurrently Preempts Queries (hidden) states Asserts (hidden) state System model Example: The model-based program sets the state to thrusting, and the M-B executive . . .:  Example: The model-based program sets the state to thrusting, and the M-B executive . . . Determines that valves on the backup engine will achieve thrust, and plans needed actions. Deduces that a valve failed - stuck closed Plans actions to open six valves Fuel tank Oxidizer tank Deduces that thrust is off, and the engine is healthy Mode Estimation:  sw1=off, sw2=off, or=nom sw1=off, sw2=off, or=bkn sw1=off, sw2=off, or=bkn t=0 t=1 t=2 cmd = sw1-turnOn Obs = LED-off cmd = sw2-turnOn Obs = LED-off … likelihood Concurrent Constraint Automata Switch and OR-Gate System Mode Estimation Purpose: Ideal mode estimation would maintain a complete belief state Belief State: Probability distribution across all combinations of possible states in the system Challenge: Combination of states is exponential in the number of modes Solution: Tracking only an approximate belief state containing k estimates (shown below) reduces the space requirement to linear while maintaining the majority of probability density! Tracking the most likely system states over time sw1=bkn, sw2=off, or=nom sw1=off, sw2=bkn, or=nom sw1=bkn, sw2=bkn, or=nom sw1=bkn, sw2=bkn, or=nom sw1=bkn, sw2=bkn, or=bkn k=3 Compiled Mode Estimation:  Compiled Mode Estimation Dissent: A mapping from observations to conflicts Off-line Operations (Removes the need for NP-complete online satisfiability) (LED=off)  sw1=on  sw2=on  or=nom .... Model Compilation Partial Diagnosis Trigger Most Likely Diagnosis: Or-gate = Nominal Switch1 = On Switch2 = Broken Compiled Mode Estimation Offline Projected Prime Implicate Generation Online Partial Diagnosis Trigger Best-First Belief State Enumeration Monitors System Model Most Likely Diagnosis Conflicts Dissents Discrete Observations Continuous Observations Compiled Transitions Enabled Modes (LED=off) On-line Operations (Reduced to an optimal search instead of OCSP) Hybrid Mode Estimation:  Hybrid Mode Estimation failures can manifest themselves through coupling between a system’s continuous dynamics and its evolution through different behavior modes must track over continuous state changes and discrete mode changes symptoms initially on the same scale as sensor/actuator noise need to extract mode estimates from subtle symptoms old estimate: Xk-1={mi,xk-1} X+k-1={mj,xk-1} new estimate: Xk={mj,xk} Hybrid Mode Estimation tracks a set of trajectories Kalman Filter Bank yc(k) uc(k-1) Mode Estimation xci(k) Pi(k) ^ Ck Xk ^ Methods: K-best filtering, Rao-Blackwellised particle filtering Application: Gesture Recognition:  Application: Gesture Recognition Gesture recognition: Stereo vision system Tracks head and hand motion of human associate Hybrid model supports Robonaut’s recognition of human gestures Gestures of interest include pointing to a tool, holding hand up to indicate stop, “come closer” gestures, etc. Continuous dynamics model of human arm includes inertial and damping terms HMM model takes output of stereo vision system as observation Transitions between motion control point states Robonaut: EVA astronaut’s assistant Humanoid design requires no specialized robotic tools Controlled by teleoperator, but autonomous modes under development Mode Reconfiguration:  Mode Reconfiguration INPUT Configuration Goal Thrust = on Current State Tank = full Pressure = nominal Driver = off Valve = closed Thruster = off OUTPUT Command Turn driver on Goal Interpreter:  Goal Interpreter INPUT Current State Tank = full Pressure = nominal Driver = off Valve = closed Thruster = off Configuration Goal Thrust = on OUTPUT Goal State Tank = full Pressure = nominal Driver = off Valve = on Thruster = on Generate optimal goal state that achieves the Configuration Goal! Goal Interpreter Compiled Goal Interpreter Partial Goal Interpretation Best-first Kernel Goal State Generator Minimize online deduction by generating all partial goal interpretation offline! Online: Goal State Goal Configuration Reactive Planner:  Reactive Planner INPUT Current State Tank = full Pressure = nominal Driver = off Valve = closed Thruster = off Goal State Tank = full Pressure = nominal Driver = off Valve = on Thruster = on fail Goal fail driver = on cmd = open idle idle driver = on cmd = close Current Open Closed Stuck Open Closed Goal cmd = on idle idle cmd = off Current On Off Resettable On Off cmd = reset cmd = off Valve Driver OUTPUT Command Turn driver on Reconfiguration Order Tank = full Pressure = nominal Valve = on Thruster = on Driver = off Planner guarantees to: Only generate non-destructive actions Never propose actions that lead to dead-end plans Ensure progress toward the goal Operate at reactive time scale Verification of RMPL Programs:  Motivation: Want robust autonomous systems. Extend traditional scenario-based testing to verification and validation (V&V). Goals: Verify RMPL model-based programs (control program + plant model) against goal specification. e.g., ((EngineA = Firing) OR (EngineB = Firing)) for OrbitInsert() Extract probabilistic information about program’s possible executions. Verification of RMPL Programs Approach: Heterogeneous Robots:  Orbiter: Earth Com link, Large scale feature detection Science observation Tethered Blimp: Reconnaissance: Rover tracking, feature detection, local map generator Sensor network deployment Rover Com link Smart Mobile Lander Slow mobile base station Orbiter Com link Large science package Scout Rovers Fast agile rovers Sensor package for identifying science objectives Terrain mapping functionality Sensor Network Highly constrained sensing/effecting communication array Science sensing Aids in robot localization High Tier Low Tier Mid Tier Heterogeneous Robots Programming Cooperative Teams:  Collection Point Rendezvous Diverge Science Area 2 Science Area 3 Science Area 1 The system must handle … Task allocation between robots Planning of activities and vehicle paths Robust execution Programming Cooperative Teams Realistic science objectives require multiple vehicles Mission controller specifies abstract set of goals for a robot team Challenges … Dynamic environments Limited communication between robots Hardware failure Programming Teams in RMPL:  Programming Teams in RMPL (Group-Enroute() [l,u] ( (sequence choose ( (do-watching (PATH1=OK) ((Group-Traverse-Path(PATH1_1,PATH1_2,PATH1_3,RE_POS))[l*90%,u*90%]) ) (do-watching (PATH2=OK) ((Group-Traverse-Path(PATH2_1,PATH2_2,PATH2_3,RE_POS))[l*90%,u*90%]) )) (parallel ((Group-Transmit(OPS,ARRIVED))[0,2]) (do-watching(PROCEED=SIGNALLED) ((Group-Wait(HOLD1,HOLD2))[0,u*10%])) ))) Rendezvous Rescue Area Corridor 2 Corridor 1 Enroute RMPL Programs Describe concurrent sensing, actuation and movements activities. Choose specifies redundant strategies and contingencies. [A,B] Specifies timing constraints. Planning and Execution:  Temporal Planner Planning and Execution RMPL Compiler Plan Runner/ Dispatcher Temporal Plan Network RMPL Program Temporally Flexible Solution Plan Hardware Commands Hardware Mission Specification Planning and Execution (choose (parallel ((power = high) [5,30]) (goTo(rockA) [10,30]) ) (if-then-else (camera = on) (takePicture() [5,5]) (powerOnCamera() [6,8]) ) ) Represents all possible contingencies, with non-deterministic choices and temporal constraints Challenges: Synchronization Robustness Real-time control of dynamic systems Path Planning through Disjunctive Programming:  Path Planning through Disjunctive Programming A simple example: The input plan includes logical (discrete) decisions, such as task selection, temporal orderings, and obstacle avoidance. Vehicle/Terrain models involve mathematical (continuous) constraints. Our goal is to output for the vehicles a trajectory and schedule plan that optimizes the total fuel use, based on the discrete and continuous constraints. It is formulated in Disjunctive Programming (DP), which can be viewed as Linear Programming constrained by disjunctive clauses. Vehicles have to go from point A to C, without hitting the obstacle B, while minimizing the fuel consumed. The disjunctive clause comes from the fact that the vehicles can be above, below, to the left or right of B. Minimize f(x) Subject to g(x) ≤ 0 xi ≤ xL V xi ≥ xR V yi ≤ yB V yi ≥ yT ,  i = 1, …, n Distributed Planning and Execution:  Distributed Planning and Execution mission A Distributed System … Eliminates dependency on a single robot for planning and execution Shares computation to allow execution on groups of robots where each has limited computational resources Allows coordination under limited communication availability Scales well to large groups of robots Challenges … Coordination and synchronization Maintaining temporally flexible plans Adaptation to loss of a robot Adaptation to changing communication availability Mars Shadow Mode Project:  Mars Shadow Mode Project Simulate Mission Objectives of Mars ’03 Use NASA’s MERBoard to visualize the environment and control the rovers. Demonstrate the ability to achieve mission goals autonomously Analyze this rock! Rover Sensors Stereo camera head Laser range scanner Sonar array DGPS Digital compass Inclinometer Remote operations center Mars yard

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