Online Motion Planning for Multi-robot Interaction Using Composable Reachable Sets
This paper presents an algorithm for autonomous online path planning in uncertain, possibly adversarial, and partially observable environments. In contrast to many state-of-the-art motion planning approaches, our focus is on decision making in the presence of adversarial agents who may be acting strategically but whose exact behaviour is difficult to model precisely. Our algorithm first computes a collection of reachable sets with respect to a family of possible strategies available to the adversary. Online, the agent uses these sets as composable behavioural templates, in conjunction with a particle filter to maintain the current belief on the adversary’s strategy. In partially observable environments, this yields significant performance improvements over state-of-the-art planning algorithms. We present empirical results to this effect using a robotic soccer simulator, highlighting the applicability of our implementation against adversaries with varying capabilities. We also demonstrate experiments on the NAO humanoid robots, in the context of different collision-avoidance scenarios.
KeywordsOnline Motion Planning Autonomous Decision Making Composable Behavioural Templates
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- 1.Supporting material, http://www.youtube.com/watch?v=BfJgWz4TwlE
- 2.NAO robot documentation, http://academics.aldebaran-robotics.com/
- 3.RoboCup SPL rules, pp. 1–27 (2010), http://www.tzi.de/spl/
- 5.Bruce, J.: Real-Time Motion Planning and Safe Navigation in Dynamic Multi-Robot Environments. Ph.D. thesis, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA (January 2006)Google Scholar
- 6.Burridge, R.R., Rizzi, A.A., Koditschek, D.E.: Sequential composition of dynamically dexterous robot behaviors. IJRR 18(6), 534–555 (1999)Google Scholar
- 7.Ding, J., Tomlin, C.J.: Trajectory optimization in convex underapproximations of safe regions. In: CDC, pp. 2510–2515 (2009)Google Scholar
- 9.Fox, D., Burgard, W., Thrun, S.: The dynamic window approach to collision avoidance. IEEE RAM 4(1), 23–33 (1997)Google Scholar
- 13.Tedrake, R.: LQR-trees: Feedback motion planning on sparse randomized trees. In: Proceedings of Robotics: Science and Systems, Seattle, USA (June 2009)Google Scholar
- 14.Tomlin, C.J., Lygeros, J., Sastry, S.S.: A game theoretic approach to controller design for hybrid systems. Proc. IEEE, 949–970 (2000)Google Scholar
- 15.Tomlin, C.J., Mitchell, I., Bayen, A.M., Oishi, M.: Computational techniques for the verification of hybrid systems. Proc. IEEE, 986–1001 (2003)Google Scholar
- 16.Varaiya, P.: Hierarchical control of semi-autonomous teams under uncertainty. Final report of Darpa Contract F33615-01-C-3150 (2004)Google Scholar