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Robust Decentralized Planning for Large-Scale Heterogeneous Human-Robot Teams

Robust Decentralized Planning for Large-Scale Heterogeneous Human-Robot Teams. Prof. Jonathan P. How Department of Aeronautics and Astronautics Massachusetts Institute of Technology. Motivation. Network centric operations involve teams of heterogeneous agents executing several tasks

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Robust Decentralized Planning for Large-Scale Heterogeneous Human-Robot Teams

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  1. Robust Decentralized Planning for Large-Scale Heterogeneous Human-Robot Teams Prof. Jonathan P. How Department of Aeronautics and AstronauticsMassachusetts Institute of Technology

  2. Motivation • Network centric operations involve teams of heterogeneous agents executing several tasks • Goal: Automate task allocation to improve performance of large heterogeneous networked teams • Spatial and temporal synchronization • Reduce mission costs • Robust to dynamic environments • Lots of literature in HRI, VRP (VRPTW) & scheduling • Mostly focused on one operator controlling multiple UVs • Usually reactive to system demands Second Year Review 09/10/2010

  3. Task Allocation Planner • Approach:Predictive systems-level planning • Distributed planning for all agents • Temporal constraints and task dependencies • Account for availability of operators/agents • Robustness to modeling uncertainties • Efficient for real-time replanning • Key Technical Challenges: • Complex combinatorial decision (NP-hard) • Large dynamic networks with asynchronous communication constraints • Stochastic, non-linear, time-varying agents • Uncertain and dynamic environments Second Year Review 09/10/2010

  4. Consensus-Based Bundle Algorithm • Developed a new decentralized planning approach called Consensus-Based Bundle Algorithm(CBBA) [Choi, Brunet, How 2009] • Focus on agreement of plans rather than just information • Task allocation algorithm iterates between 2 phases • Phase 1: Bundle Building and Bidding (greedy selection of tasks) • Phase 2: Consensus (conflict resolution) • Core features of CBBA: • Polynomial-time decentralized decision algorithm • Guaranteed convergence on task assignments with inconsistent SA • Provably good approximate solutions for multi-agent multi-task allocation problems • Extended to include temporal constraints (e.g. task time-windows) [ACC 2010] Phase 1: Build Bundle & Bid on Tasks Phase 2: Consensus Yes Assigned? No Second Year Review 09/10/2010

  5. Consensus-Based Bundle Algorithm • CBBA successfully used in real-time flight test environments • Cooperative search, acquisition, and track (CSAT) • Coordination of agents under dynamic network topologies • Further information available online at: acl.mit.edu/projects/cbba.html Second Year Review 09/10/2010

  6. Robust CBBA for Human-Robot Teams • Interested in task allocation for human-robot teams • Issue: Human operator performance is stochastic [Cummings, Southern ‘10] • Heterogeneous operator capabilities (“slow” vs. “fast” operators) • Planner should be robust to uncertainty in team performance • Assume known probabilistic distributions on service time (log-normal) • Key Challenge: Incorporate uncertainty into planner to increase robustness Log-Normal Distribution for Operator Target Identification Figure from [D. Southern, Masters Thesis, 2010] Second Year Review 09/10/2010

  7. Robust CBBA for Human-Robot Teams • Stochastic planning framework: • Cost function for each task ( ) is function of the path ( ) and fixed service time for operator i ,( ), which is a R.V. ~ • Issue: Computationally intractable • Cost approximation in planner: • Function of a constant value ( ) • Represents overall team performance • Assumes homogeneous operators • Need to choose (Typically: ) Second Year Review 09/10/2010

  8. Robust CBBA for Human-Robot Teams • To better understand the impact of ( ), analyzed mission score as a function of expected vs. actual operator service times • Scenario details: • Team of 5 homogeneous operators (similar service times) • Operators sampled from percentile ranges (e.g. 70% - 80%) • Planned using values of expected operator performance ( ) corresponding to different percentile ranges and evaluated for several actual operator ranges Fast prediction performs better on fast teams Conservativeplanning is less variable than Optimisticplanning Performance drops quickly for slower teams Second Year Review 09/10/2010

  9. Robust CBBA for Human-Robot Teams • Performed Monte-Carlo experiments over varying operator percentile ranges (for both expected and actual) • Observations: • Fast operator teams achieved higher scores • Performance is highest when expected and actual match exactly (ridge line) • Steeper drop for overestimating (optimistic) vs. underestimating (conservative) • Conservative planning is better than optimistic planning Second Year Review 09/10/2010

  10. Robust CBBA for Human-Robot Teams • Previously assumed homogeneous agents • In reality have heterogeneous team • Percentile sampling doesn’t capture uncertainty in team • Sample from entire distribution: • Use Dirichlet distribution to bias team towards different ends of the log-normal • Performance has more variance than before (less “peaky”), but similar trends • Conservative is better than Optimistic • Robust planning framework • Conservative: mitigate worse case over subset A (function of mean and variance of estimate) Second Year Review 09/10/2010

  11. Robust CBBA for Human-Robot Teams • Can plan conservatively to increase robustness but still limited • Don’t know expected team performance ( ) • If team model is inaccurate, planner performance is arbitrarily bad • Need to estimate • Solution: Robust Adaptive Planning • Estimate overall team performance to increase planner accuracy • Adaptation gain should account for level of uncertainty (covariance) • Add margin of conservatism to increase planner robustness • Results show that performance is close to maximum (ridge line) • Robust Adaptive Planning increases mission performance Second Year Review 09/10/2010

  12. Asynchronous Extensions of CBBA • Large heterogeneous teams often use unreliable communication networks • Asynchronous CBBA (ACBBA):Provide consistent and efficient handling of asynchronous messages [Johnson, GNC 2010] • Key features of ACBBA: • New set of local deconfliction rules • Agents run CBBA on their own schedule (speeds up convergence) • Parallelized execution of both CBBA phases • Agents broadcast only relevant information (reduces bandwidth) • Handles dynamic network topologies naturally • Reduces communication load while preserving convergence properties Second Year Review 09/10/2010

  13. Asynchronous Extensions of CBBA Currently extending ACBBA framework in several ways: • Augmenting ACBBA to include coupling between tasks (Coupled-Constraint CBBA [Whitten, Masters Thesis, 2010]) • Ensuring convergence in an asynchronous replanning framework • Paradigm shift  Continuous nature of ACBBA • Accounting for DMG complicates this further • Verifying tactical performance of ACBBA on actual flight vehicles Second Year Review 09/10/2010

  14. MIT-Cornell Collaboration • Combined task allocation, trajectory planning and sensor fusion • CBBA used to allocate targets to the vehicles (MIT) • IRRT used to plan information-rich vehicle trajectories (MIT) • Image processing and sensor fusion was used to update the target PDFs (Cornell) • Human-in-the-loop performed classification and PDF updates through HRI (Cornell) Second Year Review 09/10/2010

  15. MIT-Cornell Collaboration • Search and ID mission with 2 robots, 1 human, and 5 targets • Robots: Pioneer 3-DX with camera and LIDAR sensors • Human: Sends soft info and target class to robots via GUI • Robots search autonomously over PDFs of target locations • Human-robot data fusion over Gaussian mixture PDFs (Cornell) • Decentralized task allocation, information-rich path planning (MIT) • MIT team went to Cornell for initial set of indoor experiments Second Year Review 09/10/2010

  16. Conclusions • Summary: Distributed systems-level predictive task allocation for networks of heterogeneous agents • Distributed planning for all agents • Directly account for availability of operators/agents to improve mission efficiency • Robustness to modeling uncertainties • Handles dynamic networks and asynchronous communication environments • Computationally efficient: real-time replanning capability • Key extensions of CBBA: • Robust Planning Framework • Asynchronous CBBA • Collaborated with Cornell to combine task allocation, trajectory planning and sensor fusion • Human-robot data fusion over Gaussian mixture PDFs (Cornell) • Decentralized task allocation and information-rich path planning (MIT) • Verified feasibility in a real-time flight test environment Second Year Review 09/10/2010

  17. Publications • S. Ponda, H.-L. Choi, and J. P. How, “Predictive Planning for Heterogeneous Human-Robot Teams”, AIAA Infotech@Aerospace, April 2010. • S. Ponda, J. Redding, H.-L. Choi, J. P. How, M. A. Vavrina, and J. Vian, “Decentralized Planning for Complex Missions with Dynamic Communication Constraints”, American Controls Conference, July 2010. • L. B. Johnson, S. Ponda, H.-L. Choi, and J. P. How, “Improving the Efficiency of a Decentralized Tasking Algorithm for UAV Teams with Asynchronous Communications”, AIAA Conference on Guidance, Navigation and Control, August 2010. • S. Ponda, O. Huber, H.-L. Choi, J. P. How, “Avoid Communication Outages in Decentralized Planning”, IEEE Global Communications Conference, December 2010 (to appear). Second Year Review 09/10/2010

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