Decentralized Multi-agent Path Selection Using Minimal Information

Conference paper
First Online:
Part of the Springer Tracts in Advanced Robotics book series (STAR, volume 112)

Abstract

This work studies conflict avoidance between moving, non-communicating agents with minimum sensing information. While safety can be provided by reactive obstacle avoidance methods for holonomic systems, deadlock avoidance requires reasoning over different homotopic paths in cluttered scenes. A method to compute the “interaction cost” of a path is proposed, which considers only the neighboring agents’ observed positions. Minimizing the interaction cost in a prototypical challenge with two agents moving through two corridors from opposing sides guarantees the selection of non-conflicting paths. More complex scenes, however, are more challenging. This leads to a study of alternatives for decentralized path selection. Simulations indicate that following a “minimum-conflict” path given the other agents’ observed positions provides deadlock avoidance. A scheme that selects between the minimum-conflict path and a set of shortest paths given their interaction cost improves path quality while still achieving deadlock avoidance. Finally, learning to select between the minimum-conflict and one of the shortest paths allows agents to be adaptive to the behavior of their neighbors and can be achieved using regret minimization.

Keywords

Multi-agent Decentralized Coordination Path planning 
This is a preview of subscription content, log in to check access.

References

  1. 1.
    Bekris, K.E., Grady, D.K., Moll, M., Kavraki, L.E.: Safe distributed motion coordination for second-order systems with different planning cycles. IJRR 31(2) (2012)Google Scholar
  2. 2.
    Bhattacharya, S., Kumar, V., Likhachev, M.: Search-based path planning with homotopy class constraints. In: Third Annual Symposium on Combinatorial Search (2010)Google Scholar
  3. 3.
    Bhattacharya, S., Likhachev, M., Kumar, V.: Identification and representation of homotopy classes of trajectories for search-based path planning in 3D. In: RSS (2011)Google Scholar
  4. 4.
    Bhattacharya, S., Likhachev, M., Kumar, V.: Topological constraints in search-based robot path planning. Auton. Robots 33(3), 273–290 (2012)CrossRefGoogle Scholar
  5. 5.
    Fiorini, P., Shiller, Z.: Motion planning in dynamic environments using velocity obstacles. IJRR 17(7) (1998)Google Scholar
  6. 6.
    Fraichard, T., Delsart, V.: Navigating dynamic environments with trajectory deformation. J. Comput. Inf. Technol. 17(1) (2009)Google Scholar
  7. 7.
    Green, C., Kelly, A.: Toward optimal sampling in the space of paths. In: ISRR (2007)Google Scholar
  8. 8.
    Hatcher, A.: Algebraic Topology. Cambridge University Press (2002)Google Scholar
  9. 9.
    Hauser, K.: Adaptive time stepping in real-time motion planning. In: Algorithmic Foundations of Robotics IX, pp. 139–155. Springer (2011)Google Scholar
  10. 10.
    Hauser, K.: Minimum constraint displacement motion planning. In: RSS (2013)Google Scholar
  11. 11.
    Henry, P., Vollmer, C., Ferris, B., Fox, D.: Learning to navigate through crowded environments. In: ICRA. Anchorage, AK (2010)Google Scholar
  12. 12.
    Jaillet, L., Siméon, T.: Path deformation roadmaps. In: Workhop on the Algorithmic Foundations of Robotics (WAFR) (2006)Google Scholar
  13. 13.
    Kaminka, G., Erusalimchik, D., Kraus, S.: Adaptive multi-robot coordination: a game-theoretic perspective. In: ICRA (2010)Google Scholar
  14. 14.
    Karaman, S., Frazzoli, E.: Sampling-based algorithms for optimal motion planning. In: IJRR (2011)Google Scholar
  15. 15.
    Karamouzas, I., Geraerts, R., Overmars, M.: Indicative routes for path planning and crowd simulation. In: Foundations of Digital Games (FDG), pp. 113–120 (2009)Google Scholar
  16. 16.
    Kimmel, A., Dobson, A., Bekris, K.E.: Maintaining team coherence under the velocity obstacle framework. In: Autonomous Agents and Multiagent Systems (AAMAS) (2012)Google Scholar
  17. 17.
    Kimmel, A., Dobson, A., Littlefield, Z., Krontiris, A., Marble, J., Bekris, K.E.: Pracsys: an extensible architecture for composing motion controllers and planners. In: SIMPAR (2012)Google Scholar
  18. 18.
    Knepper, R.A., Mason, M.T.: Path diversity is only part of the problem. In: ICRA (2009)Google Scholar
  19. 19.
    Knepper, R.A., Rus, D.: Pedestrian-inspired sampling-based multi-Robot collision avoidance. In: IEEE RO-MAN, pp. 94–100. IEEE, Paris, France (2012)Google Scholar
  20. 20.
    Knepper, R.A., Rus, D.: On the completeness of ensembles of motion planners for decentralized planning. In: ICRA. Karlsruhe, Germany (2013)Google Scholar
  21. 21.
    Littlestone, N., Warmuth, M.K.: The weighted majority algorithm. Inf. Comput. 108, 212–261 (1994)MathSciNetCrossRefMATHGoogle Scholar
  22. 22.
    Nisan, N., Roughgarden, T., Tardos, E., Vazirani, V.V.: Algorithmic Game Theory. Cambridge University Press (2007)Google Scholar
  23. 23.
    Petti, S., Fraichard, T.: Partial motion planning framework for reactive planning within dynamic environments. In: ICINCO, pp. 199–204 (2005)Google Scholar
  24. 24.
    Qutub, S., Alami, R., Ingrand, F.: How to solve deadlock situations within the plan-merging paradigm for multi-Robot cooperation. IROS 3, 1610–1615 (1997)Google Scholar
  25. 25.
    Shi, D., Collins, E.G., Donate, A., Liu, X., Goldiez, B., Dunlap, D.: Human-aware Robot motion planning with velocity constraints. In: IEEE International Symposium on Collaborative Technologies and Systems, pp. 490–497 (2008)Google Scholar
  26. 26.
    Sisbot, E.A., Marin-Urias, L.F., Alami, R., Siméon, T.: A human-aware mobile Robot motion planner. IEEE Trans. Robot. 23(5), 874–883 (2007)CrossRefGoogle Scholar
  27. 27.
    Snape, J., van Den Berg, J., Guy, S., Manocha, D.: The hybrid reciprocal velocity obstacle. IEEE Trans. Robot. 27(4), 696–706 (2011)CrossRefGoogle Scholar
  28. 28.
    Thompson, S., Horiuchi, T., Kagami, S.: A probabilistic model of human motion and navigation intent for mobile Robot path planning. In: ICARA (2009)Google Scholar
  29. 29.
    van Den Berg, J., Patil, S., Sewall, J., Manocha, D., Lin, M.: Interactive navigation of individual agents in crowded environments. In: I3D (2008)Google Scholar
  30. 30.
    van den Berg, J., Lin, M., Manocha, D.: Reciprocal velocity obstacles for real-time multi-agent navigation. In: Proceedings of the IEEE International Conference on Robotics and Automation (ICRA) (2008)Google Scholar
  31. 31.
    van den Berg, J., Snape, J., Guy, S., Manocha, D.: Reciprocal collision avoidance with acceleration-velocity obstacles. In: ICRA (2011)Google Scholar
  32. 32.
    Ziebart, B.D., Ratliff, N., Gallagher, G., Mertz, C., Peterson, K., Bagnell, J.A., Hebert, M., Dey, A., Srinivasa, S.: Planning-based prediction for pedestrians. In: IROS (2009)Google Scholar

Copyright information

© Springer Japan 2016

Authors and Affiliations

  1. 1.Rutgers UniversityPiscatawayUSA

Personalised recommendations