Copyright B. Williams 16.412J/6.834J, Fall 03 Introduction To Cognitive Robots Prof. Brian Williams Prof. Nick Roy Wednesday, February 4 th , 2004 Copyright B. Williams 16.412J/6.834J, Fall 03 Outline ? Course Objectives ? Student Introductions and Goals ? Example: Robots as Explorers ? Example: Human Robot Interaction Copyright B. Williams 16.412J/6.834J, Fall 03 Course Objective 1 To understand the main types of cognitive robots and their driving requirements: ? Robots That Navigate – Hallway robots, Field robots, Underwater explorers, stunt air vehicles ? Engineering and “Immobile” Robots – Intelligent spaces – Robust space probes ? Cooperating Robots – Cooperative Space/Air/Land/Underwater vehicles, distributed traffic networks, smart dust. Accomplished by: ? Case studies during lectures ? Supports course final project (Objective 4). Copyright B. Williams 16.412J/6.834J, Fall 02 Immobile Robots in Space Image courtesy of NASA. Copyright B. Williams 16.412J/6.834J, Fall 03 Autonomous Systems use Models to Anticipate or Detect Subtle Failures 600 700 800 900 1000 1100 1200 1300 1400 400 500 600 700 800 900 1000 1100 1200 time (minutes) CO 2 con cen tr ati on ( pp m) crew requests entry to plant growth chamber crew enters chamber lighting fault crew leaves chamber NASA Mars Habitat Airlock Plant Growth Chamber Crew Chamb e r CO 2 tank lighting system chamber control flow regulator 2 pulse injection valves CO 2 flow regulator 1 Copyright B. Williams 16.412J/6.834J, Fall 03 courtesy NASA Ames Image courtesy of NASA. Copyright B. Williams 16.412J/6.834J, Fall 03 Course Objective 2 ? To understand fundamental methods for creating the major capabilities of cognitive robots. Accomplished by: ?~ Lectures on core methods ?~ 3-4 problem sets to exercise basic understanding of methods. Plan Execute Monitor & Diagnosis Locate in World Navigate Map Copyright B. Williams 16.412J/6.834J, Fall 03 Topics On Cognitive Robot Capabilities ? Robots that Plan and Act in the World – Robots that Deftly Navigate – Planning and Executing Complex Missions ? Robots that Are State-Aware – Robots that Find Their Way In The World – Robots that Deduce Their Internal State ? Robots that Preplan For An Uncertain Future – Theoretic Planning in a Hidden World – State and Fault Aware Systems Copyright B. Williams 16.412J/6.834J, Fall 03 Course Objective 3 ? To dive into the recent literature, and collectively synthesize, clearly explain and evaluate the state of the art in intelligent embedded systems. Accomplished by: ?Group lectures on advance topic ?40 minute or 80 minute lectures ?tutorial article on ~2 methods, to support lectures. ?Groups of size ~2. Copyright B. Williams 16.412J/6.834J, Fall 03 Course Objective 4 ? To apply one or more core reasoning methods to create a simple agent that is driven by Goals or Rewards Accomplished by: ? Final project during last third of course ? Implement and demonstrate one or more reasoning methods in a simple cognitive robot scenario (simulated or hardware). ? Final project report. ? Short project demonstration. Plan Execute Monitor & Diagnosis Locate in World Navigate Map Copyright B. Williams 16.412J/6.834J, Fall 03 Outline ? Course Objectives ? Student Introductions and Goals ? Example: Robots as Explorers ? Example: Human Robot Interaction Copyright B. Williams 16.412J/6.834J, Fall 03 Outline ? Course Objectives ? Student Introductions and Goals ? Example: Robots as Explorers ? Example: Human Robot Interaction Robotic Space Explorers: To Boldly Go Where No AI System Has Gone Before A Story of Survival 16.412J/6.834J September 19, 2001 Copyright B. Williams 16.412J/6.834J, Fall 03 Readings and Assignment Readings: ? Remote Agent: to Boldy Go Where No AI System Has Gone Before, N.Muscettola, P. Nayak, B. Pell and B. Williams, Artificial Intelligence 103 (1998) 5-47. Copyright B. Williams 16.412J/6.834J, Fall 03 Outline ? Motivation ? Model-based autonomous systems ? Remote Agent Example Cryobot & Hydrobot courtesy JPL Europa ``Our vision in NASA is to open the Space Frontier . . . We must establish a virtual presence, in space, on planets, in aircraft and spacecraft.’’ - Daniel S. Goldin, NASA Administrator, May 29, 1996 courtesy JPL Distributed Spacecraft Interferometers search for Earth-like Planets Around Other Stars Copyright B. Williams 16.412J/6.834J, Fall 03 Miscommanded: ? Mars Climate Orbiter ? Clementine courtesy of JPL Spacecraft should watch out for their own survival. Cassini Maps Titan courtesy JPL ? 7 year cruise ? ~ 150 - 300 ground operators ?~ 1 billion $ ? 7 years to build A Capable Robotic Explorer: Cassini ?150 million $ ?2 year build ? 0 ground ops Faster, Better, Cheaper courtesy JPL Mars Pathfinder and Sojourner Copyright B. Williams 16.412J/6.834J, Fall 03 Four launches in 7 months Mars Climate Orbiter: 12/11/98 Mars Polar Lander: 1/3/99 Stardust: 2/7/99 QuickSCAT: 6/19/98 courtesy of JPL Copyright B. Williams 16.412J/6.834J, Fall 03 Objective: Support programmers with embedded languages that avoid these mistakes, by reasoning about hidden state automatically. Leading Diagnosis: ? Legs deployed during descent. ? Noise spike on leg sensors latched by software monitors. ? Laser altimeter registers 40m. ? Begins polling leg monitors to determine touch down. ? Latched noise spike read as touchdown. ? Engine shutdown at ~40m. Reactive Model-based Programming Language (RMPL) Mars Polar Lander Failure Programmers often make commonsense mistakes when reasoning about hidden state. Image courtesy of JPL. Copyright B. Williams 16.412J/6.834J, Fall 03 Traditional spacecraft commanding GS,SITURN,490UA,BOTH,96-355/03:42:00.000; CMD,7GYON, 490UA412A4A,BOTH, 96-355/03:47:00:000, ON; CMD,7MODE, 490UA412A4B,BOTH, 96-355/03:47:02:000, INT; CMD,6SVPM, 490UA412A6A,BOTH, 96-355/03:48:30:000, 2; CMD,7ALRT, 490UA412A4C,BOTH, 96-355/03:50:32:000, 6; CMD,7SAFE, 490UA412A4D,BOTH, 96-355/03:52:00:000, UNSTOW; CMD,6ASSAN,490UA412A6B,BOTH, 96-355/03:56:08:000, GV,153,IMM,231, GV,153; CMD,7VECT, 490UA412A4E,BOTH, 96-355/03:56:10.000, 0,191.5,6.5, 0.0,0.0,0.0, 96-350/ 00:00:00.000,MVR; SEB,SCTEST,490UA412A23A,BOTH, 96-355/03:56:12.000, SYS1,NPERR; CMD,7TURN, 490UA412A4F,BOTH, 96-355/03:56:14.000, 1,MVR; MISC,NOTE, 490UA412A99A,, 96-355/04:00:00.000, ,START OF TURN;, CMD,7STAR, 490UA412A406A4A,BOTH 96-355/04:00:02.000, 7,1701, 278.813999,38.74; CMD,7STAR, 490UA412A406A4B,BOTH,96-355/04:00:04.000, 8,350,120.455999, -39.8612; CMD,7STAR, 490UA412A406A4C,BOTH,96-355/04:00:06.000, 9,875,114.162, 5.341; CMD,7STAR, 490UA412A406A4D,BOTH,96-355/04:00:08.000, 10,159,27.239, 89.028999; CMD,7STAR, 490UA412A406A4E,BOTH,96-355/04:00:10.000, 11,0,0.0,0.0; CMD,7STAR, 490UA412A406A4F,BOTH,96-355/04:00:12.000, 21,0,0.0,0.0; What Makes this Difficult: Cassini Case Study courtesy JPL Reasoning through interactions is complex Houston, We have a problem ... courtesy of NASA ? Quintuple fault occurs (three shorts, tank-line and pressure jacket burst, panel flies off). ? Mattingly works in ground simulator to identify new sequence handling severe power limitations. ? Mattingly identifies novel reconfiguration, exploiting LEM batteries for power. ? Swaggert & Lovell follow novel procedure to repair Apollo 13 lithium hydroxide unit. Survival can require replanning the complete mission on the fly. Helium tank Fuel tankOxidizer tank Main Engines Flow 1 = zero Pressure 1 = nominal Pressure 2 = nominal Acceleration = zero Copyright B. Williams 16.412J/6.834J, Fall 03 Challenge: Thinking Through Interactions Programmers must reason through system-wide interactions to generate codes for: ? command confirmation ? goal tracking ? detecting anomalies ? isolating faults ? diagnosing causes ? hardware reconfig ? fault recovery ? safing ? fault avoidance ? control coordination Equally problematic at mission operations level Copyright B. Williams 16.412J/6.834J, Fall 03 Outline ? Motivation ? Model-based autonomous systems ? Remote Agent Example Copyright B. Williams 16.412J/6.834J, Fall 03 Course Objective 2 ? To understand fundamental methods for creating the major components of intelligent embedded systems. Plan Execute Monitor & Diagnosis Copyright B. Williams 16.412J/6.834J, Fall 03 Programmers generate breadth of functions from commonsense models in light of mission goals. ? Model-based Programming ? Program by specifying commonsense, compositional declarative models. ? Model-based Planning, Execution and Monitoring ? Provide services that reason through each type of system interaction from models. ? on the fly reasoning requires significant search & deduction within the reactive control loop. Model-based Autonomy courtesy of NASA ? Quintuple fault occurs (three shorts, tank-line and pressure jacket burst, panel flies off). ? Mattingly works in ground simulator to identify new sequence handling severe power limitations. ? Mattingly identifies novel reconfiguration, exploiting LEM batteries for power. ? Swaggert & Lovell work on Apollo 13 emergency rig lithium hydroxide unit. Styles of Thinking Through Interactions ? Quintuple fault occurs (three shorts, tank-line and pressure jacket burst, panel flies off). ? Mattingly works in ground simulator to identify new sequence handling severe power limitations. ? Mattingly identifies novel reconfiguration, exploiting LEM batteries for power. ? Swaggert & Lovell work on Apollo 13 emergency rig lithium hydroxide unit. ? Multiple fault diagnosis of unexperienced failures. ? Mission planning and scheduling ? Hardware reconfiguration ? Scripted execution Styles of Thinking Through Interactions Copyright B. Williams 16.412J/6.834J, Fall 03 Example of a Model-based Robot: ? Goal-directed ? First time correct ? projective ? reactive ? Commonsense models ? Heavily deductive Scripts component models Goals Diagnosis & Repair Mission Manager Executive Planner/ Scheduler Remote Agent Mission-level actions & resources Copyright B. Williams 16.412J/6.834J, Fall 03 Conventional Wisdom: Reservations about Intelligent Embedded Systems ? “[For reactive systems] proving theorems is out of the question” [Agre & Chapman 87] Copyright B. Williams 16.412J/6.834J, Fall 03 Many problems aren’t so hard Copyright B. Williams 16.412J/6.834J, Fall 03 Generate Non-conflicting Successor Generate Non-conflicting Successor Candidates with decreasing likelihood or value SAT SAT Generalization of Conflicts Generalization of Conflicts Developed RISC-like, deductive kernel (OPSAT) How can general deduction achieve reactive time scales? Solutions When you have eliminated the impossible, whatever remains, however improbable [costly], must be the truth. - Sherlock Holmes. The Sign of the Four. ((Image removed due to copyright considerations.) Copyright B. Williams 16.412J/6.834J, Fall 03 Transition Systems + Constraints + Probabilities Closed Valve Open Stuck open Stuck closed Open Close 0. 01 0. 01 0.01 0.01 inflow = outflow = 0 Can model-based agents perform many different types of reasoning from a common model? VDECU Op_State On Engine Op State Burn_Ignition Burn Burn_Termination Shut_down Off Early_Prep Wait Late_Prep Copyright B. Williams 16.412J/6.834J, Fall 03 Outline ? Motivation ? Model-based autonomous systems ? Remote Agent Example Copyright B. Williams 16.412J/6.834J, Fall 03 Real-Time Execution Real-Time Execution Flight H/W Flight Fault Monitors Fault Monitors Planning Experts (incl. Navigation) Planning Experts (incl. Navigation) Remote Agent Architecture Ground System Ground System RAX Manager Diagnosis & Repair Mission Manager Executive Planner/ Scheduler Remote Agent RAX_START Copyright B. Williams 16.412J/6.834J, Fall 03 Real-Time Execution Real-Time Execution Flight H/W Flight Fault Monitors Fault Monitors Planning Experts (incl. Navigation) Planning Experts (incl. Navigation) Ground System Ground System RAX Manager Diagnosis & Repair Mission Manager Executive Planner/ Scheduler Remote Agent RAX_START Executive requests plan Copyright B. Williams 16.412J/6.834J, Fall 03 Real-Time Execution Real-Time Execution Flight H/W Flight Fault Monitors Fault Monitors Planning Experts (incl. Navigation) Planning Experts (incl. Navigation) Ground System Ground System RAX Manager Diagnosis & Repair Mission Manager Executive Planner/ Scheduler Remote Agent RAX_START Remote Agent Architecture Copyright B. Williams 16.412J/6.834J, Fall 03 Real-Time Execution Real-Time Execution Flight H/W Flight Fault Monitors Fault Monitors Planning Experts (incl. Navigation) Planning Experts (incl. Navigation) Ground System Ground System RAX Manager Diagnosis & Repair Mission Manager Executive Planner/ Scheduler Remote Agent RAX_START Mission manager establishes goals, planner generates plan Copyright B. Williams 16.412J/6.834J, Fall 03 Real-Time Execution Real-Time Execution Flight H/W Flight Fault Monitors Fault Monitors Planning Experts (incl. Navigation) Planning Experts (incl. Navigation) Ground System Ground System RAX Manager Diagnosis & Repair Mission Manager Executive Planner/ Scheduler Remote Agent RAX_START Executive executes plan Copyright B. Williams 16.412J/6.834J, Fall 03 Real-Time Execution Real-Time Execution Flight H/W Flight Fault Monitors Fault Monitors Planning Experts (incl. Navigation) Planning Experts (incl. Navigation) Ground System Ground System RAX Manager Diagnosis & Repair Mission Manager Executive Planner/ Scheduler Remote Agent RAX_START Diagnosis system monitors and repairs Walk Through of Cassini Saturn Orbital Insertion Copyright B. Williams 16.412J/6.834J, Fall 03 Real-Time Execution Real-Time Execution Flight H/W Flight Fault Monitors Fault Monitors Planning Experts (incl. Navigation) Planning Experts (incl. Navigation) Ground System Ground System RAX Manager Diagnosis & Repair Mission Manager Executive Planner/ Scheduler Remote Agent RAX_START Plan for Next Time Horizon Image courtesy of JPL. Copyright B. Williams 16.412J/6.834J, Fall 03 Thrust Goals Attitude Engine Power Copyright B. Williams 16.412J/6.834J, Fall 03 Thrust Goals Attitude Point(a) Engine OffOff Delta_V(direction=b, magnitude=200) Power Mission Manager Sets Goals over Horizon Copyright B. Williams 16.412J/6.834J, Fall 03 Planner Repeatedly Applies Library of Operational Constraints Thrust Goals Engine Thrust (b, 200) Delta_V(direction=b, magnitude=200) contains Copyright B. Williams 16.412J/6.834J, Fall 03 Thrust Goals Attitude Point(b) Engine Thrust (b, 200) Off Delta_V(direction=b, magnitude=200) Power Warm Up meets met_by contained_by contained_by equals Planner Repeatedly Applies Library of Operational Constraints Copyright B. Williams 16.412J/6.834J, Fall 03 Thrust Goals Attitude Point(a) Engine OffOff Delta_V(direction=b, magnitude=200) Power Planner Starts Copyright B. Williams 16.412J/6.834J, Fall 03 Thrust Goals Attitude Point(a) Engine Thrust (b, 200) OffOff Delta_V(direction=b, magnitude=200) Power Copyright B. Williams 16.412J/6.834J, Fall 03 Thrust Goals Attitude Point(a) Engine Delta_V(direction=b, magnitude=200) Power Thrust (b, 200) OffOff Copyright B. Williams 16.412J/6.834J, Fall 03 Thrust Goals Attitude Point(a) Engine Delta_V(direction=b, magnitude=200) Power Thrust (b, 200) OffOff Copyright B. Williams 16.412J/6.834J, Fall 03 Thrust Goals Attitude Point(a) Engine Delta_V(direction=b, magnitude=200) Power OffOff Thrust (b, 200) Copyright B. Williams 16.412J/6.834J, Fall 03 Point(b) Thrust Goals Attitude Point(a) Engine Off Delta_V(direction=b, magnitude=200) Power Thrust (b, 200) Off Copyright B. Williams 16.412J/6.834J, Fall 03 Thrust Goals Attitude Point(a) Point(b) Engine Thrust (b, 200)Off Delta_V(direction=b, magnitude=200) Power Off Copyright B. Williams 16.412J/6.834J, Fall 03 Thrust Goals Attitude Point(a) Point(b) Engine Thrust (b, 200)Warm Up OffOff Delta_V(direction=b, magnitude=200) Power Copyright B. Williams 16.412J/6.834J, Fall 03 Thrust Goals Attitude Point(a) Point(b) Turn(b,a) Engine Thrust (b, 200)Warm Up OffOff Delta_V(direction=b, magnitude=200) Power Copyright B. Williams 16.412J/6.834J, Fall 03 Thrust Goals Attitude Point(a) Point(b) Turn(b,a) Engine Thrust (b, 200)Warm Up OffOff Delta_V(direction=b, magnitude=200) Power Copyright B. Williams 16.412J/6.834J, Fall 03 Thrust Goals Attitude Turn(a,b) Point(a) Point(b) Turn(b,a) Engine Thrust (b, 200) OffOff Delta_V(direction=b, magnitude=200) Power Warm Up Copyright B. Williams 16.412J/6.834J, Fall 03 Thrust Goals Attitude Turn(a,b) Point(a) Point(b) Turn(b,a) Engine Thrust (b, 200) OffOff Delta_V(direction=b, magnitude=200) Power Warm Up Copyright B. Williams 16.412J/6.834J, Fall 03 Thrust Goals Attitude Turn(a,b) Point(a) Point(b) Turn(b,a) Engine Thrust (b, 200) OffOff Delta_V(direction=b, magnitude=200) Power Warm Up Plan Completed! Copyright B. Williams 16.412J/6.834J, Fall 03 Plans Allow Temporal Flexibility Through Least Committment [1035, 1035] [130,170] <0, 0> [0, 300] [0, + ∞ ] [0, + ∞] [0, 0] Copyright B. Williams 16.412J/6.834J, Fall 03 Real-Time Execution Real-Time Execution Flight H/W Flight Fault Monitors Fault Monitors Planning Experts (incl. Navigation) Planning Experts (incl. Navigation) Ground System Ground System RAX Manager Diagnosis & Repair Mission Manager Scripted Executive Planner/ Scheduler Remote Agent RAX_START The executive dynamically schedules and dispatches tasks Copyright B. Williams 16.412J/6.834J, Fall 03 Executing Temporal Plans [130,170]] <0, 0> [0, 300] [0, + ] [0, + ] [0, 0] ? Propagate temporal constraints ? Select enabled events ? Terminate preceding activities ? Run next activities Copyright B. Williams 16.412J/6.834J, Fall 03 Propagating Timing Constraints Can Be Costly EXECUTIVE CONTROLLED SYSTEM Copyright B. Williams 16.412J/6.834J, Fall 03 EXECUTIVE CONTROLLED SYSTEM Propagating Timing Constraints Can Be Costly Copyright B. Williams 16.412J/6.834J, Fall 03 EXECUTIVE CONTROLLED SYSTEM Propagating Timing Constraints Can Be Costly Copyright B. Williams 16.412J/6.834J, Fall 03 EXECUTIVE CONTROLLED SYSTEM Propagating Timing Constraints Can Be Costly Copyright B. Williams 16.412J/6.834J, Fall 03 EXECUTIVE CONTROLLED SYSTEM Propagating Timing Constraints Can Be Costly Copyright B. Williams 16.412J/6.834J, Fall 03 EXECUTIVE CONTROLLED SYSTEM Propagating Timing Constraints Can Be Costly Copyright B. Williams 16.412J/6.834J, Fall 03 EXECUTIVE CONTROLLED SYSTEM Solution: Compile Temporal Constraints to an Efficient Network Copyright B. Williams 16.412J/6.834J, Fall 03 EXECUTIVE CONTROLLED SYSTEM Solution: Compile Temporal Constraints to an Efficient Network Copyright B. Williams 16.412J/6.834J, Fall 03 EXECUTIVE CONTROLLED SYSTEM Solution: Compile Temporal Constraints to an Efficient Network Copyright B. Williams 16.412J/6.834J, Fall 03 Real-Time Execution Real-Time Execution Flight H/W Flight Fault Monitors Fault Monitors Planning Experts (incl. Navigation) Planning Experts (incl. Navigation) Ground System Ground System RAX Manager Diagnosis & Repair Mission Manager Scripted Executive Planner/ Scheduler Remote Agent RAX_START Execution and Fault Recovery involves monitoring and commanding hidden states Copyright B. Williams 16.412J/6.834J, Fall 03 Programmers and operators must reason through system-wide interactions to generate codes for: Reconfiguring ModesEstimating Modes ? monitoring ? tracking goals ? confirming commands ? detecting anomalies ? diagnosing faults ? reconfiguring hardware ? coordinating control policies ? recovering from faults ? avoiding failures Model-based Execution of Activities Copyright B. Williams 16.412J/6.834J, Fall 03 Model-based Execution as Stochastic Optimal Control Controller Plant mode Estimation mode reconfiguration s’(t) μ(t) f s(t) g o(t) Model Livingstone Goals Copyright B. Williams 16.412J/6.834J, Fall 03 Closed Open Stuck open Stuck closed Open Close Cost 5 Prob .9 Models ? modes engage physical processes ? encoded as finite domain constraints ? probabilistic automata for dynamics ? Concurrency to model multiple processes Vlv = closed => Outflow = 0; vlv=open => Outflow = M z + (inflow); vlv=stuck open => Outflow = M z + (inflow); vlv=stuck closed=> Outflow = 0; Fuel tankOxidizer tank Copyright B. Williams 16.412J/6.834J, Fall 03 Reconfiguring for a Failed Engine Copyright B. Williams 16.412J/6.834J, Fall 03 Reconfiguring for a Failed Engine Open four valves Fuel tankOxidizer tank Copyright B. Williams 16.412J/6.834J, Fall 03 Reconfiguring for a Failed Engine Valve fails stuck closed Open four valves Fuel tankOxidizer tank Copyright B. Williams 16.412J/6.834J, Fall 03 Reconfiguring for a Failed Engine Fire backup engine Valve fails stuck closed Open four valves Fuel tankOxidizer tank Copyright B. Williams 16.412J/6.834J, Fall 03 Possible Behaviors Visualized by a Trellis Diagram S T X 0 X 1 X N-1 X N ?Assigns a value to each variable. ?Consistent with all state constraints. ?A set of concurrent transitions, one per automata. ?Previous & Next states consistent with source & target of transitions Copyright B. Williams 16.412J/6.834J, Fall 03 Model-based Execution as Stochastic Optimal Control Controller Plant mode Estimation mode reconfiguration s’(t) μ(t) f s(t) g o(t) Model Livingstone Goals Fire backup engine Valve fails stuck closed S T X0 X1 XN-1 XN S T X0 X1 XN-1 XN least cost reachable goal state First ActionCurrent Belief State Control Sequencer Deductive Controller System Model Commands Observations Control Program Plant Titan Model-based ExecutiveRMPL Model-based Program State goalsState estimates Control Sequencer: Generates goal states conditioned on state estimates Mode Estimation: Tracks likely States Mode Reconfiguration: Tracks least-cost state goals z Executes concurrently z Preempts z Asserts and queries states z Chooses based on reward Fire backup engine Valve fails stuck closed S T X0 X1 XN-1 XN S T X0 X1 XN-1 XN least cost reachable goal state First ActionCurrent Belief State Copyright B. Williams 16.412J/6.834J, Fall 03 Mode Estimation and Diagnosis Observe “no thrust” Find most likely reachable states consistent with observations. Copyright B. Williams 16.412J/6.834J, Fall 03 Mode Reconfiguration and Repair Goal: Achieve Thrust Copyright B. Williams 16.412J/6.834J, Fall 03 Goal: Achieve Thrust Mode Reconfiguration and Repair Copyright B. Williams 16.412J/6.834J, Fall 03 Goal: Achieve Thrust Mode Reconfiguration and Repair courtesy JPL Ames-JPL NewMaap Demonstration: New Millennium Advanced Autonomy Prototype July - November, 1995 Copyright B. Williams 16.412J/6.834J, Fall 03 Remote Agent on Deep Space 1 Started: January 1996 Launch: Fall 1998 Image courtesy of JPL. Image courtesy of JPL. Copyright B. Williams 16.412J/6.834J, Fall 03 Remote Agent Experiment May 17-18th experiment ? Generate plan for course correction and thrust ? Diagnose camera as stuck on – Power constraints violated, abort current plan and replan ? Perform optical navigation ? Perform ion propulsion thrust May 21th experiment. ? Diagnose faulty device and – Repair by issuing reset. ? Diagnose switch sensor failure. – Determine harmless, and continue plan. ? Diagnose thruster stuck closed and – Repair by switching to alternate method of thrusting. ? Back to back planning See rax.arc.nasa.gov Copyright B. Williams 16.412J/6.834J, Fall 03 Mars Exploration Rovers – Jan. 2004 Mission Objectives: ? Learn about ancient water and climate on Mars. ? For each rover, analyze a total of 6-12 targets – Targets = natural rocks, abraded rocks, and soil ? Drive 200-1000 meters per rover ? Take 1-3 panoramas both with Pancam and mini-TES ? Take 5-15 daytime and 1-3 nightime sky observations with mini-TES Mini-TES Pancam Navcam Rock Abrasion Tool Microscopic Imager Mossbauer spectrometer APXS Image courtesy of JPL. Copyright B. Williams 16.412J/6.834J, Fall 03 Mars Exploration Rover Surface Operations Scenario Target Day 4 During the Day Science Activities Day 1 Long-Distance Traverse (<20-50 meters) Day 2 Initial Position; Followed by “Close Approach” During the Day Autonomous On- Board Navigation Changes, as needed Day 2 Traverse Estimated Error Circle Day 3 Science Prep (if Required) Day 2 Traverse Estimated Error Circle Activity Name Durati on 101121314151617181920212230123456789 101121314151617181920212230123456789 DTE 4.50 0.75 DTE period DFE Night Time Rover Operations 16.97 Night Time Rover OperationsSleep Wakeup Pre-Comm Session Sequence Plan Review Current Sol Sequence Plan Review 1.50 1.50 Current Sol Sequence Plan Review Prior Sol Sequence Plan Review 2.00 Prior Sol Sequence Plan Review Real-TIme Monitoring 4.50 0.75 Real-TIme Monitoring Real-TIme Monitoring Downlink Product Generation... 2.75 Downlink Product Generation Tactical Science Assessment/Observation Planning 5.00 Tactical Science Assessment/Observation Planning Science DL Assessment Meeting 1.00 Science DL Assessment Meeting Payload DL/UL Handoffs 0.50 Payload DL/UL Handoffs Tactical End-of-Sol Engr. Assessment & Planning 5.50 Tactical End-of-Sol Engr. Assessment & Planning DL/UL Handover Meeting 0.50 DL/UL Handover Meeting Skeleton Activity Plan Update 2.50 Skeleton Activity Plan Update SOWG Meeting 2.00 SOWG Meeting Uplink Kickoff Meeting 0.25 Uplink Kickoff Meeting Activity Plan Integration & Validation 1.75 Activity Plan Integration & Validation Activity Plan Approval Meeting 0.50 Activity Plan Approval Meeting Build & Validate Sequences 2.25 Build & Validate Sequences UL1/UL2 Handover 1.00 UL1/UL2 Handover Complete/Rework Sequences 2.50 Complete/Rework Sequences Margin 1 0.75 Margin 1 Command & Radiation Approval 0.50 Command & Radiation Ap Margin 2 1.25 Margin 2 Radiation 0.50 Radiation MCT Team 7.00 4.00 One day in the life of a Mars rover Courtesy: Jim Erickson Downlink Assessment Science Planning Sequence Build/Validation Uplink Copyright B. Williams 16.412J/6.834J, Fall 03 EUROPA Automated Planning System EUROPA Automated Planning System Science Navigation Engineering Resource Constraints DSN/Telcom Flight Rules Science Team Sequence Build MAPGEN: Automated Science Planning for MER Planning Lead: Kanna Rajan (ARC) Copyright B. Williams 16.412J/6.834J, Fall 03 One Project Challenge ? What would it be like to operate MER if it was fully autonomous? Course project: ? Demonstrate an autonomous MER mission in simulation, and in the MIT rover testbed. Copyright B. Williams 16.412J/6.834J, Fall 03 Next Challenge: Mars Smart Lander (2009) Mission Duration: 1000 days Total Traverse: 3000-69000 meters Meters/Day: 230-450 Science Mission: 7 instruments, sub-surface science package (drill, radar), in-situ sample “lab” Technology Demonstration: (2005). Image courtesy of JPL.