Multi-robot Cooperative Pathfinding: A Decentralized Approach

  • Changyun Wei
  • Koen V. Hindriks
  • Catholijn M. Jonker
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8481)


When robots perform teamwork in a shared workspace, they might be confronted with the risk of blocking each other’s ways, which will result in conflicts or interference among the robots. How to plan collision-free paths for all the robots is the major challenge issue in the multi-robot cooperative pathfinding problem, in which each robot has to navigate from its starting location to the destination while keeping avoiding stationary obstacles as well as its teammates. In this paper, we present a novel fully decentralized approach to this problem. Our approach allows the robots to make real-time responses to the dynamic environment and can resolve a set of benchmark deadlock situations subject to complex spatial constraints in the robots’ workspace. When confronted with conflicting situations, robots can employ waiting, dodging, retreating and turning-head strategies to make local adjustments. In addition, experimental results show that our proposed approach provides an efficient and competitive solution to this problem.


Cooperative pathfinding coordination collision avoidance 


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  1. 1.
    Kaminka, G.A.: Autonomous agents research in robotics: A report from the trenches. In: 2012 AAAI Spring Symposium Series (2012)Google Scholar
  2. 2.
    Standley, T., Korf, R.: Complete algorithms for cooperative pathfinding problems. In: Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence, pp. 668–673 (2011)Google Scholar
  3. 3.
    Surynek, P.: An optimization variant of multi-robot path planning is intractable. In: AAAI (2010)Google Scholar
  4. 4.
    Desaraju, V.R., How, J.P.: Decentralized path planning for multi-agent teams with complex constraints. Autonomous Robots 32, 385–403 (2012)CrossRefGoogle Scholar
  5. 5.
    Van Den Berg, J.P., Overmars, M.H.: Prioritized motion planning for multiple robots. In: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 430–435 (2005)Google Scholar
  6. 6.
    Luna, R., Bekris, K.E.: Efficient and complete centralized multi-robot path planning. In: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 3268–3275. IEEE (2011)Google Scholar
  7. 7.
    de Wilde, B., ter Mors, A.W., Witteveen, C.: Push and rotate: cooperative multi-agent path planning. In: Proceedings of the Twelfth International Conference on Autonomous Agents and Multiagent Systems, pp. 87–94 (2013)Google Scholar
  8. 8.
    Parker, L.E.: Decision making as optimization in multi-robot teams. In: Ramanujam, R., Ramaswamy, S. (eds.) ICDCIT 2012. LNCS, vol. 7154, pp. 35–49. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  9. 9.
    Parker, L.E.: Current state of the art in distributed autonomous mobile robotics. In: Distributed Autonomous Robotic Systems 4, pp. 3–12. Springer (2000)Google Scholar
  10. 10.
    Silver, D.: Cooperative pathfinding. In: The First Conference on Artificial Intelligence and Interactive Digital Entertainment (AIIDE), pp. 117–122 (2005)Google Scholar
  11. 11.
    Wang, K.H.C., Botea, A.: Mapp: a scalable multi-agent path planning algorithm with tractability and completeness guarantees. Journal of Artificial Intelligence Research 42, 55–90 (2011)zbMATHMathSciNetGoogle Scholar
  12. 12.
    Ryan, M.R.: Exploiting subgraph structure in multi-robot path planning. Journal of Artificial Intelligence Research 31, 497–542 (2008)zbMATHGoogle Scholar
  13. 13.
    Burgard, W., Moors, M., Stachniss, C., Schneider, F.E.: Coordinated multi-robot exploration. IEEE Transactions on Robotics 21, 376–386 (2005)CrossRefGoogle Scholar
  14. 14.
    Bennewitz, M., Burgard, W., Thrun, S.: Finding and optimizing solvable priority schemes for decoupled path planning techniques for teams of mobile robots. Robotics and Autonomous Systems 41, 89–99 (2002)CrossRefGoogle Scholar
  15. 15.
    Zuluaga, M., Vaughan, R.: Reducing spatial interference in robot teams by local-investment aggression. In: IEEE/RSJ International Conference on Intelligent Robots and Systems(IROS), pp. 2798–2805 (2005)Google Scholar
  16. 16.
    Dresner, K., Stone, P.: Multiagent traffic management: a reservation-based intersection control mechanism. In: Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems, pp. 530–537 (2004)Google Scholar
  17. 17.
    Wang, K.H.C., Botea, A.: Fast and memory-efficient multi-agent pathfinding. In: International Conference on Automated Planning and Scheduling (ICAPS), pp. 380–387 (2008)Google Scholar
  18. 18.
    Johnson, M., Jonker, C., van Riemsdijk, B., Feltovich, P.J., Bradshaw, J.M.: Joint activity testbed: Blocks world for teams (BW4T). In: Aldewereld, H., Dignum, V., Picard, G. (eds.) ESAW 2009. LNCS, vol. 5881, pp. 254–256. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  19. 19.
    Hindriks, K.: The goal agent programming language (2013),

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Changyun Wei
    • 1
  • Koen V. Hindriks
    • 1
  • Catholijn M. Jonker
    • 1
  1. 1.Interactive Intelligence Group, EEMCSDelft University of TechnologyDelftThe Netherlands

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