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Distributed Path Planning for Collective Transport Using Homogeneous Multi-robot Systems

  • Golnaz HabibiEmail author
  • William Xie
  • Mathew Jellins
  • James McLurkin
Conference paper
Part of the Springer Tracts in Advanced Robotics book series (STAR, volume 112)

Abstract

We present a scalable distributed path planning algorithm for transporting a large object through an unknown environment using a group of homogeneous robots. The robots are randomly scattered across the terrain and collectively sample the obstacles in the environment in a distributed fashion. Given this sampling and the dimensions of the bounding box of the object, the robots construct a distributed configuration space. We then use a variant of the distributed Bellman-Ford algorithm to construct a shortest-path tree using a custom cost function from the goal location to all other connected robots. The cost function encompasses the work required to rotate and translate the object in addition to an extra control penalty to navigate close to obstacles. Our approach sets up a framework that allows the user to balance the trade-off between the safety of the path and the mechanical work required to move the object. The path is optimal given the sampling of the robots and user input parameters. We implemented our algorithm in both simulated and real-world environments. Our approach is robust to the size and shape of the object and adapts to dynamic environments.

Keywords

Path planning Distributed algorithm Distributed bellman-ford algorithm Multi-robot system Collective transport 

Notes

Acknowledgments

The authors would like to thank Zachary Kingston for his tremendous help in running experiments on real robots. This work has been supported by National Science Foundation, Division of Computer and Network Systems under CNS-1330085.

References

  1. 1.
    Cheng, C., Riley, R., Kumar, S.P.R, Garcia-Aceves, J.J.: A loop-free extended Bellman-Ford routing protocol without bouncing effect. In: SIGCOMM ’89 Symposium Proceedings on Communications Architectures and Protocols, vol. 19, pp. 224–236 (1989)Google Scholar
  2. 2.
    Clark, B.N., Colbourn, C.J., Johnson, D.S.: Unit disk graphs. Discr. Math. 86(1–3), 165–177 (1990)Google Scholar
  3. 3.
    Fabbri, R., Estrozi, L.F.: On Voronoi diagrams and medial axes. J. Math. Imaging Vis. 17, 27–40 (2002)MathSciNetCrossRefzbMATHGoogle Scholar
  4. 4.
    Fekete, S.P., Kamphans, T., Kröller, A., Mitchell, J.S.B., Schmidt, C.: Exploring and triangulating a region by a swarm of robots. In: 14th International Workshop, 2011, and 15th International Workshop, pp. 206–217, Princeton, NJ, USA (2011)Google Scholar
  5. 5.
    Ford, L., Fulkerson, D., Bland, R.: Flows in Networks, ser, Princeton Landmarks in Mathematics. Princeton University Press, Princeton (2010)Google Scholar
  6. 6.
    Kamio, S., Iba, H.: Random sampling algorithm for multi-agent cooperation planning. In: IROS, pp. 1265–1270. IEEE (2005)Google Scholar
  7. 7.
    Kamio, S., Iba, H.: Cooperative object transport with humanoid robots using rrt path planning and re-planning. In: IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 2608–2613. IEEE (2006)Google Scholar
  8. 8.
    Kavraki, L., Svestka, P., claude Latombe, J., Overmars, M.: Probabilistic roadmaps for path planning in high-dimensional configuration spaces. In: ICRA, pp. 566–580 (1996)Google Scholar
  9. 9.
    Kleinrock, L., Silvester, J.: Optimum transmission radii for packet radio networks or why six is a magic number. In: Conference Record, National Telecommunications Conference, pp. 4.3.2–4.3.5, Birmingham, Alabama, Dec 1978Google Scholar
  10. 10.
    LaValle, S.M.: Rapidly-exploring random trees: a new tool for path planning. Computer Science Department, Iowa State University, Technical report (1998)Google Scholar
  11. 11.
    Mayya, N., Rajan, V.T.: Voronoi diagrams of polygons: a framework for shape representation. J. Math. Imaging Vis. 6(4), 355–378 (1996)MathSciNetCrossRefGoogle Scholar
  12. 12.
    McLurkin, J.: Analysis and implementation of distributed algorithms for multi-robot systems. Ph.D. dissertation, MIT, USA (2008)Google Scholar
  13. 13.
    McLurkin, J., McMullen, A., Robbins, N., Habibi, G., Becker, A., Chou, A., Li, H., John, M., Okeke, N., Rykowski, J., et al.: A robot system design for low-cost multi-robot manipulation. In: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2014), pp. 912–918. IEEE (2014)Google Scholar
  14. 14.
    O’Hara, K.J.O., Balch, T.R.: Distributed path planning for robots in dynamic environments using a pervasive embedded network. In: Proceedings of the Thrid International Joint Conference on Autonomous Agent and Multiagent Systems, pp. 1538–1539, July 2004Google Scholar
  15. 15.
    Parra-González, E.F., Ramírez-Torres, J.G., Toscano-Pulido, G.: A new object path planner for the box pushing problem. In: Electronics, Robotics and Automotive Mechanics Conference: CERMA’09, pp. 119–124. IEEE (2009)Google Scholar
  16. 16.
    Reina, A., Di Caro, G.A., Ducatelle, F., Gambardella, L.M.: Distributed motion planning for ground objects using a network of robotic ceiling cameras. In: Towards Autonomous Robotic Systems, pp. 137–148. Springer (2011)Google Scholar
  17. 17.
    Yamashita, A., Arai, T., Ota, J., Asama, H.: Motion planning of multiple mobile robots for cooperative manipulation and transportation. IEEE Trans. Robot. Autom. 19, 223–237 (2003)CrossRefGoogle Scholar

Copyright information

© Springer Japan 2016

Authors and Affiliations

  • Golnaz Habibi
    • 1
    Email author
  • William Xie
    • 2
  • Mathew Jellins
    • 3
  • James McLurkin
    • 1
  1. 1.Department of Computer ScienceRice UniversityHoustonUSA
  2. 2.Department of Computer ScienceUniversity of Texas at AustinAustinUSA
  3. 3.Purdue UniversityWest LafayetteUSA

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