16.851 Satellite Engineering Fall 2003 Massachusetts Institute of Technology Spacecraft Autonomy Seung H. Chung 2 Massachusetts Institute of Technology Why Autonomy? ? Failures ? Anomalies ? Communication ? Coordination courtesy of NASA JPL Europa ProbeNew Horizons Courtesy of the Johns Hopkins University courtesy of NASA Apollo 13 Quintuple fault (three shorts, tank- line and pressure jacket burst, panel flies off). Mars Polar Lander courtesy of NASA JPL Mars Outpost courtesy of NASA JPL Applied Physics Laboratory. Used with permission. 3 Massachusetts Institute of Technology Autonomy Technologies ? Fault Detection, Isolation and Recovery ? Planning & Scheduling ? Intelligent Data Understanding ? Path Planning – Gradient method – Mixed integer linear programming (Prof John How) – Graph search (Prof Brian Williams) ? Localization & Mapping – Concurrent mapping and localization (Prof John Leonard) 4 Massachusetts Institute of Technology Why Fault Detection Isolation & Recovery (FDIR)? ? Improve the likelihood of mission success by minimizing the downtime. – Increase productivity – Prevent loss of opportunities – Reduce safety risk ? For manned missions, longer system downtime implies higher risk to the astronauts. 5 Massachusetts Institute of Technology FDIR Techniques ? If-then-else – Hard coded set of FDIR statements ? Rule-based – Set of rules written by the engineers – Fires a rule (i.e. executes a rule) when the rule is satisfied –Example ? #24 (ID > 1A) And (Ishunt_D > 6A) for 10 sec, then Try_Sec_Bus_Reg_Off. ? #27 (Red Battery Charger is ON) for 5 sec, then rule (28,29) stop. – The core software is reusable. – Engineers must enumerate all possible faults and combinations thereof along with the corresponding recovery methods. – Verifying the validity of the rules is difficult. 6 Massachusetts Institute of Technology Model-based FDIR Technique ? Engineers model the behavior of the system (i.e. components). ? Computer detects/isolates/recovers faults by reasoning on the model of the system. ? Both the model and the model-based FDIR system can be reused. ? Problem too difficult for a computer? Model-based FDIR System Observation Command 7 Massachusetts Institute of Technology Planning & Scheduling ? Planning –Given: ? Set of actions a system can perform and the associated requirements and effects of the actions ? Current state ? Desired goal state – Objective: Compute a sequence of actions that achieves the desired goal state. ? Scheduling – Given: Set of tasks to execute and the associated constraints (i.e. time, resource, …) – Objective: Compute the proper order of the tasks that satisfies the constraints. 8 Massachusetts Institute of Technology Planning Example ? Goal: Take an image of Alpha Centauri ? Plan: 1. Compute current position and attitude 2. Compute the necessary position and attitude for Alpha Centauri to be in view 3. Initialize and warm-up the imaging system 4. Change the position and point toward Alpha Centauri 5. Open the shutter 6. Take image 9 Massachusetts Institute of Technology Why Planning & Scheduling? ? Simplify spacecraft commanding. ? Simplify mission operations work. ? Enable timely replanning when necessary without communication time-delay issues. 10 Massachusetts Institute of Technology Intelligent Data Understanding ? What is it? – Knowledge Discovery: Is this something new, something interesting? – Pattern Recognition: What are the identifiable characteristics? – Classification and Clustering: Does this belong to some category of information? ?Why? – The communication bandwidth does not allow transmission of all available data. – Serendipitous events… 11 Massachusetts Institute of Technology Remote Agent Experiment 16.851 Satellite Engineering Fall 2003 Massachusetts Institute of Technology Model-based Embedded and Robotic Systems Group 13 Massachusetts Institute of Technology Model-based Programs Reason in Terms of State Embedded programs interact with the system’s sensors/actuators: ? Read sensors ? Set actuators Model-based programs interact with the system’s state: ? Read state ? Set state Embedded Program S Plant Obs Cntrl Model-based Embedded Program S Plant S’ Model-based Executive Obs Cntrl Programmer must map between state and sensors/actuators. M-B Executive maps between states and sensors/actuators. 14 Massachusetts Institute of Technology Model-based Programming Example EngineA EngineB Science Camera EngineA EngineB Science Camera ? goal: fire one of the two engines ? set both engines to ‘standby’ ? prior to firing the engine, turn the camera off to avoid plume contamination ? in case of engine failure, fire the backup 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) Systems engineers think in terms of state trajectories: Engineers reason how to achieve state trajectories using component models 0.01 0.01 ResettableResettabl reset- cmd 15 Massachusetts Institute of Technology Model-based Executive “Executable Specification” Mode Estimation Command Configuration Goals Observation Mode Reconfiguration State Estimate System Sequencer EngineA EngineB Science Camera ? goal: fire one of the two engines ? set both engines to ‘standby’ ?prior tofiring the engine, turn the camera off to avoid plume contamination ? in case of engine failure, fire the backup 16 Massachusetts Institute of Technology Mode Estimation S 3 S 2 S 1 Configuration Goal: Engine A = Firing Observation: Thrust = 0 Engine A Engine A Engine A Engine A Possible Diagnoses 17 Massachusetts Institute of Technology Mode Reconfiguration Goal Interpreter Reactive Planner Configuration goals Goal State Command Current State INPUT ? Configuration Goal –Trust = on ? Current State – Tank = full – Pressure = nominal – Driver = off – Valve = closed – Thruster = off OUPUT ? Command – Turn driver on N 2 H 4 GHe P SDriver 18 Massachusetts Institute of Technology 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 m 1 τ 21 τ 12 τ 23 τ 13 m 3 m 2 τ 22 τ 11 τ 33 Hidden Markov Models Continuous Dynamics 1 1 1 (1) ((),(),() : () ( (), ()) ( 1) ( (), (), ()) : () ( (), ()) ccccc cccc cciccc i ccicc x k f xkukvk m yk g xkvk x k f xkukvk m yk g xkvk +=? ? ? ? = ?? +=? ? ? ? = ?? M Hybrid Model 19 Massachusetts Institute of Technology Difficulty with Autonomy ? Most problems require exponential time… – Unacceptable for real-time systems that have hard-time requirement ? Possible Approach – Use divide-and-conquer approach – Provide additional knowledge that guides the search for solution – Use suboptimal solution – Perform the difficult computations offline and execute the results online