Advertisement

Any-Com Multi-robot Path-Planning with Dynamic Teams: Multi-robot Coordination under Communication Constraints

  • Michael Otte
  • Nikolaus Correll
Part of the Springer Tracts in Advanced Robotics book series (STAR, volume 79)

Abstract

We are interested in finding solutions to the multi-robot path-planning problem that have guarantees on completeness, are robust to communication failure, and incorporate varying team size. In this paper we present an algorithm that addresses the complete multi-robot path-planning problem from two different angles. First, dynamic teams are used to minimize computational complexity per robot and maximize communication bandwidth between team-members. Second, each team is formed into a distributed computer that utilizes surplus communication bandwidth to help achieve better solution quality and to speed-up consensus time. The proposed algorithm is evaluated in three real-world experiments that promote dynamic team formation. In the first experiment, a five mobile robot team plans a set of compatible paths through an office environment while communication quality is disrupted using a tin-can Faraday cage. Results show that the distributed framework of the proposed algorithm drastically speeds-up computation, even when packet loss is as high as 97%. In the second and third experiments, four robots are deployed in a network of three building wings connected by a common room. Results of the latter experiments emphasize a need for dynamic team algorithms that can judiciously choose which subset of the original problem a particular dynamic team should solve.

Keywords

Mobile Robot Packet Loss Motion Planning Communication Quality Team Formation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Alami, R., Fleury, S., Herrb, M., Ingrand, F., Robert, F.: Multi-robot cooperation in the martha project. IEEE Robotics and Automation Magazine 5, 36–47 (1998)CrossRefGoogle Scholar
  2. Aronov, B., de Berg, M., van der Stappen, A.F., Svestka, P., Vleugels, J.: Motion planning for multiple robots. In: Proceedings of the Fourteenth Annual Symposium on Computational Geometry, Minneapolis, USA, pp. 374–382 (1998)Google Scholar
  3. Asarm, K., Schmidt, G.: Conflict-free motion of multiple mobile robots based on decentralized motion planning and negotiation. In: Proc. IEEE International Conference on Robotics and Automation, pp. 3526–3533 (1997)Google Scholar
  4. Bennewitz, M., Burgard, W., Thrun, S.: Optimizing schedules for prioritized path planning of multi-robot systems. In: Proc. IEEE International Conference on Robotics and Automation, pp. 271–276 (2001)Google Scholar
  5. Boddy, M., Dean, T.L.: Solving time-dependent planning problems. In: Proc. Eleventh International Joint Conference on Artificial Intelligence, pp. 979–984 (1989)Google Scholar
  6. Bonert, M., Shu, L.H., Benhabib, B.: Motion planning for multi-robot assembly systems. International Journal of Computer Integrated Manufacturing 13, 301–310 (2000)CrossRefGoogle Scholar
  7. Buckley, S.J.: Fast motion planning for multiple moving robots. In: Proc. IEEE International Conference on Robotics and Automation, pp. 322–326 (1989)Google Scholar
  8. Clark, C.M., Rock, S.: Randomized motion planning for groups of nonholonomic robots. In: Proc. International Symposium of Artificial Intelligence, Robotics and Automation in Space (2001)Google Scholar
  9. Clark, C.M., Rock, S.M., Latombe, J.C.: Dynamic networks for motion planning in multi-robot space systems. In: Proc. 7th International Symposium on Artificial Intelligence, Robotics and Automation in Space: i-SAIRAS, pp. 3621–3631 (2003a)Google Scholar
  10. Clark, C.M., Rock, S.M., Latombe, J.C.: Motion planning for multiple mobile robots using dynamic networks. In: Proc. IEEE International Conference on Robotics and Automation, pp. 4222–4227 (2003b)Google Scholar
  11. Erdmann, M., Lozano-Perez, T.: On multiple moving objects. Algorithmica, 477–521 (1987)Google Scholar
  12. Everett, H.R., Gage, D.W., Gilbreath, G.A., Laird, R.T., Smurlo, R.P.: Real-world issues in warehouse navigation. In: Proceedings of the SPIE Conference on Mobile Robots IX, Boston, MA, vol. 2352, pp. 629–634 (1994)Google Scholar
  13. Ferguson, D., Stentz, A.: Anytime rrts. In: Proc. IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 5369–5375 (2006)Google Scholar
  14. Guo, Y., Parker, L.D.: A distributed and optimal motion planning approach for multiple mobile robots. In: Proc. IEEE International Conference on Robotics and Automation, pp. 2612–2619 (2002)Google Scholar
  15. Hada, Y., Takasa, K.: Multiple mobile robot navigation using the indoor global positioning system (igps). In: Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems, Hawaii, United States, pp. 1005–1010 (2001)Google Scholar
  16. Hollinger, G., Singh, S.: Multi-robot coordination with periodic connectivity. In: Proc. IEEE International Conference on Robotics and Automation (2010)Google Scholar
  17. Kant, K., Zuker, S.W.: Trajectory planning in time-varying environments, 1: Tpp = ppp + vpp. Technical Report TR-84-7R, McGill University, Computer vision and Robotics Laboratory, Canada (1984)Google Scholar
  18. Karaman, S., Frazzoli, E.: Incremental sampling-based algorithms for optimal motion planning. In: Proc. Robotics: Science and Systems VI (2010)Google Scholar
  19. Kato, S., Nishiyama, S., Takeno, J.: Coordinating mobile robots by applying traffic rules. In: Proc. IEEE International Conference on Intelligent Robots and Systems, pp. 1535–1541 (1992)Google Scholar
  20. Lee, B.H., Lee, C.: A minimum-time trajectory planning method for two robots. IEEE Transactions on Systems, Man and Cybernetics 17, 21–32 (1987)CrossRefGoogle Scholar
  21. Leroy, S., Laumond, J.P., Simeon, T.: Multiple path coordination for mobile robots: a geometric algorithm. In: Proc. International Conference on Artificial Intelligence (1999)Google Scholar
  22. Lumelsky, V.J., Harinarayan, K.R.: Decentralized motion planning for multiple mobile robots: The cocktail party model. Autonomous Robots 4, 121–135 (1997)CrossRefGoogle Scholar
  23. O’Donnell, P.A., Lozano-Perez, T.: Deadlock-free and collision-free coordination of two robotic manipulators. In: Proc. IEEE International Conference on Robotics and Automation, Scottsdale, AZ, pp. 484–489 (1989)Google Scholar
  24. Otte, M., Correll, N.: Any-com multi-robot path-planning: Maximizing collaboration for variable bandwidth. In: Proc. 10th International Symposium on Distributed Autonomous Systems (2010)Google Scholar
  25. Parsons, D., Canny, J.: A motion planner for multiple mobile robots. In: Proc. IEEE International Conference on Robotics and Automation, vol. 1, pp. 8–13 (1990)Google Scholar
  26. Ramanathan, G., Alagar, V.S.: Algorithmic motion planning in robotics: coordinated motion of several disks amidst polygonal obstacles. In: Proc. IEEE International Conference on Robotics and Automation, pp. 514–522 (1985)Google Scholar
  27. Ryan, M.: Exploiting subgraph structure in multi-robot path planning. Journal of Artificial Intelligence Research 31, 497–542 (2008)zbMATHGoogle Scholar
  28. Sanchez, G., Latombe, J.C.: On delaying collision checking in prm planning: Application to multi-robot coordination. The International Journal of Robotics Research 21, 5–26 (2002a)CrossRefGoogle Scholar
  29. Sanchez, G., Latombe, J.C.: Using a prm planner to compare centralized and decoupled planning for multi robot systems. In: Proc. IEEE International Conference on Robotics and Automation (2002b)Google Scholar
  30. Schwartz, J.T., Sharir, M.: On the piano mover’s problem iii. coordinating the motion of several independent bodies: the special case of circular bodies amidst polygonal barriers. In: Proc. IEEE International Conference on Robotics and Automation, pp. 514–522 (1985)Google Scholar
  31. Simeon, T., Leroy, S., Laumond, J.P.: Path coordination for multiple mobile robots: A resolution-complete algorithm. IEEE Transactions on Robotics and Automation 18, 42–49 (2002)CrossRefGoogle Scholar
  32. van den Berg, J., Guy, S.J., Lin, M., Manocha, D.: Reciprocal n-body collision avoidance. In: Proc. International Symposium on Robotics Research (2009)Google Scholar
  33. Warren, C.W.: Multiple robot path coordination using artificial potential fields. In: Proc. of IEEE International Conference on Robotics and Automation, Cincinnati, OH, pp. 500–505 (1990)Google Scholar
  34. Xidias, E.K., Aspragathos, N.A.: Motion planning for multiple non-holonomic robots: a geometric approach. Robotica 26, 525–536 (2008)CrossRefGoogle Scholar
  35. Yeung, D.Y., Bekey, G.A.: A decentralized approach to the motion planning problem for multiple mobile robots. In: Proc. IEEE International Conference on Robotics and Automation, vol. 4, pp. 1779–1784 (1987)Google Scholar

Copyright information

© Springer-Verlag GmbH Berlin Heidelberg 2014

Authors and Affiliations

  1. 1.University of Colorado at BoulderBoulderUSA

Personalised recommendations