1. MIT Dept of Aeronautics and Astronautics March 21, 2003 Graduate Open House Programming Cooperative Teams To Perform Global Science HOME TWO Enroute COLLECTION POINT RENDEZVOUS Diverge SCIENCE AREA 1’ SCIENCE AREA 3 Landing Site: ABC Landing Site: XYZ ONE SCIENCE AREA 1 Mission controller specifies abstract set of goals for a robot team System must handle: Low-level planning and execution Dynamic environment Inter-vehicle communication

2. MIT Dept of Aeronautics and Astronautics March 21, 2003 Graduate Open House 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

3. MIT Dept of Aeronautics and Astronautics March 21, 2003 Graduate Open House Kirk System Overview Graph-based Temporal Planner TPN Planner Control Program Plant Model distributed plan runner w/ strong/weak controllability RMPL Program DTPN Planner Distributed Runner Distributed Kirk Cooperative Path Planning Localization Incremental STN Consistency Control Program Plant Model Tiny RMPL Cooperative Kirk Kirk plans by selecting among alternate strategies (choose). Fast planning is achieved using a novel graph representation. Kirk executes by selecting consistent execution times, monitoring outcomes and re planning on failure.

4. MIT Dept of Aeronautics and Astronautics March 21, 2003 Graduate Open House 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.

5. MIT Dept of Aeronautics and Astronautics March 21, 2003 Graduate Open House Fast Planning Using Temporal Plan Networks (TPNs) 3 6 4 5 [405,486] Ask(PATH1=OK) 12 7 Ask(PATH2=OK) 8 [405,486] [450,540] Ask(PROCEED) 11 9 10 [0,54] 12 13 [0,2] [0,  ] 1415 Tell(PATH1=OK) [450,450] 1617 Tell(PROCEED) [200,200] s e [500,800] [10,10] [0,  ] Group-Enroute Group Traverse Group Wait Group Transmit Science Target Planning searches out consistent trajectories. Satisfies all Asked conditions Checks Schedulability A TPN is created, representing all possible executions of an RMPL program.

6. MIT Dept of Aeronautics and Astronautics March 21, 2003 Graduate Open House Kirk research challenges How do we generalize the plan runner to a more rich capability for plan execution monitoring, and how does this relate to mode estimation? How do we preserve dynamic scheduling with path planning? How do we achieve global optimality in the integration of TPN planning, generative activity planning and cooperative path planning? What is the relationship between hybrid estimation and localization? How do we fold the different elements of SLAM (Simultaneous localization and mapping) into the Kirk architecture?

7. MIT Dept of Aeronautics and Astronautics March 21, 2003 Graduate Open House Distributed Planning & Execution mission Why distributed? to avoid mission being dependent on a single planning and execution robot distributed execution is essential when communication is limited resource constrained robots or sensor networks can’t run a big planning system

8. MIT Dept of Aeronautics and Astronautics March 21, 2003 Graduate Open House Distributed Planning Solution Cooperative activity program is reformulated into data structures that can be distributed to robots Robust distributed coordination of activities: choose between functionally redundant procedures coordinate temporally flexible actions Research Challenges Minimize computation & communication Limited communication environment Plan failures Robot failures RMPL Distributed CSP Rover Rovers Blimp Rover Sensors

9. MIT Dept of Aeronautics and Astronautics March 21, 2003 Graduate Open House Distributed Execution Goals –Provide methods to allow temporally flexible distributed plan execution in the presence of limited or unreliable communication due to physical isolation or communication requirements –Provide likelihood of successful execution of distributed plans Methods –Efficient methods to parse the plan while identifying regions of high interdependence –Use flexible contracts to coordinate separated plans and allow retrograde constraint propagations to update local temporal constraints –Implement efficient algorithms of for communication and contract negotiation –Develop calculate and continuously maintain stochastic measures of successful plan execution give probabilistic measures of activity durations and communication models

10. MIT Dept of Aeronautics and Astronautics March 21, 2003 Graduate Open House Globally Optimal Planning for Agile Cooperative Autonomous Air Vehicles Apartment Building on Fire Example Scenario: Urban Search and Rescue Scenario Trapped Residents Autonomous Air Vehicles (AAVs): Do not have to account for the pilots’ safety. Can be pushed to their physical limits by performing aggressive maneuvers. Improve mission costs, such as fuel, by using multiple vehicles.

11. MIT Dept of Aeronautics and Astronautics March 21, 2003 Graduate Open House Agile Vehicles Plan Optimally How can we plan for cooperative agile air vehicles while minimizing resource costs? –Extend the Kirk model-based activity planning system to plan collision-free trajectories to various locations. –Extend the model to include associated costs with activities. –Apply a best first search strategy to find the globally optimal plan (minimizing costs such as power, fuel, etc.)

12. MIT Dept of Aeronautics and Astronautics March 21, 2003 Graduate Open House Agile Teams Search for Optimal Activity and Path Plans Concurrently Integrated Activity Planning and Path Planning: Search a plan representation, called a temporal plan network, in best-first order Dynamically compute collision-free paths for those plan activities that require moving between locations and the estimated cost of flying along this path Continuously interleave activity and path planning to pursue the most promising plan. Collision-free path Cost estimate = 10 units of fuel Cost estimate = 20 units of fuel Path Planning Strategy: Uniformly explore state space with randomized kinodynamic path planner based on Rapidly-exploring Random Trees (RRTs) Maneuver Automaton: Describes a set of agile maneuvers with respect to the vehicle’s dynamics RRT Location A: start state Location B: goal state

13. MIT Dept of Aeronautics and Astronautics March 21, 2003 Graduate Open House Cooperative Planning Objectives: –Elevate role of human controller to that of a coach or wing commander, using autonomy to control individual autonomous vehicles –Use rich plan structure so that computer-generated plans can be explained back to human controllers with contingencies, alternative options, and choice justification –Support efficient distribution and communication for team missions –Include control-vector assignment with obstacle and collision avoidance

14. MIT Dept of Aeronautics and Astronautics March 21, 2003 Graduate Open House Generative Deductive Controller Deductive Controller Generative Planner plant model environment and action data state configuration goals schedulable plan with low-level commands The deductive controller takes the high-level goal specification from the sequencer and outputs a hardware-executable plan network Challenge: Need to design a planning algorithm that supports rich actions (including time and resources), returns an optimal result, and is still fast enough for use in real-time, embedded systems Control Vector Assignment

15. MIT Dept of Aeronautics and Astronautics March 21, 2003 Graduate Open House Control Vector Assignment Vehicle Waypoint Obstacle 1 2 2 2 2 3 3 5 5 4 5 Control Vector Assignment Waypoint visitation Ordering Constraints Movement Commands For each Vehicle Generative Planner allocates vehicles to each waypoint and specifies ordering constraints on waypoint visitation This must be transformed into low-level commands for vehicles that take into account obstacle avoidance and vehicle- vehicle collision avoidance

16. MIT Dept of Aeronautics and Astronautics March 21, 2003 Graduate Open House Dealing with Obstacles O1 O2 O3 O1 = L O2 = B O3 = A A simpler example: Mathematically solving the problem of vehicle control normally involves straightforward Linear Programming But the addition of obstacle avoidance introduces an Integer Programming element This makes the problem difficult to solve “online”: fast enough for actual vehicles in motion To resolve this we transform obstacles into Constraint Satisfaction Problem elements: For each obstacle, the domain is split into four regions (above/below/left/right), one of which is selected Integrating the selection of domains with the standard vehicle control leads to a an algorithm that can be used as a Hybrid CSP/LP Solver

17. MIT Dept of Aeronautics and Astronautics March 21, 2003 Graduate Open House Robonaut potential future MERS testbed Robonaut can perform or assist in extravehicular activities, reducing the need for humans to work in dangerous space environments.

18. MIT Dept of Aeronautics and Astronautics March 21, 2003 Graduate Open House 3D Simulation test-bed Simulates heterogeneous robots in a 3D world Compatible with hardware interface Extensions include: RoboSoccer RoboQuidditch

19. MIT Dept of Aeronautics and Astronautics March 21, 2003 Graduate Open House The rover test-bed Rover test-bed in MIT building 41 Rover Sensors –Stereo camera head –Sonar array –Laser range scanner –DGPS –Wheel encoders –Digital compass 1 ATRV 3 ATRV jr. A ceiling mounted stereo camera “the blimp” Motes Sensor Network