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Coordination-free Multi-robot Path Planning for Congestion Reduction Using Topological Reasoning

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  • Published: 10 July 2023
  • Volume 108, article number 50, (2023)
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Coordination-free Multi-robot Path Planning for Congestion Reduction Using Topological Reasoning
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  • Xiaolong Wang1,
  • Alp Sahin1 &
  • Subhrajit Bhattacharya  ORCID: orcid.org/0000-0001-9139-054X1 
  • 1141 Accesses

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Abstract

We consider the problem of multi-robot path planning in a complex, cluttered environment with the aim of reducing overall congestion in the environment, while avoiding any inter-robot communication or coordination. Such limitations may exist due to lack of communication or due to privacy restrictions (for example, autonomous vehicles may not want to share their locations or intents with other vehicles or even to a central server). The key insight that allows us to solve this problem is to stochastically distribute the robots across different routes in the environment by assigning them paths in different topologically distinct classes, so as to lower congestion and the overall travel time for all robots in the environment. We outline the computation of topologically distinct paths in a spatio-temporal configuration space and propose methods for the stochastic assignment of paths to the robots. A fast replanning algorithm and a potential field based controller allow robots to avoid collision with nearby agents while following the assigned path. Our simulation and experiment results show a significant advantage over shortest path following under such a coordination-free setup.

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Data Availability

The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.

References

  1. Wang, X., Sahin, A., Bhattacharya, S.: Coordination-free Multi-robot Path Planning for Congestion Reduction Using Topological Reasoning. arXiv (2022). https://doi.org/10.48550/ARXIV.2205.00955. https://arxiv.org/abs/2205.00955

  2. Silver, D.: Cooperative pathfinding. In: Proceedings of the Aaai Conference on Artificial Intelligence and Interactive Digital Entertainment, vol. 1, pp. 117–122 (2005)

  3. Sharon, G., Stern, R., Felner, A., Sturtevant, N.R.: Conflict-based search for optimal multi-agent pathfinding. Artificial Intelligence 219, 40–66 (2015)

    Article  MathSciNet  MATH  Google Scholar 

  4. Han, S.D., Yu, J.: Ddm: Fast near-optimal multi-robot path planning using diversified-path and optimal sub-problem solution database heuristics. IEEE Robotics and Automation Letters 5(2), 1350–1357 (2020)

    Article  Google Scholar 

  5. Wagner, G., Choset, H.: M*: A complete multirobot path planning algorithm with performance bounds. In: Intelligent Robots and Systems (IROS), 2011 IEEE/RSJ International Conference On, pp. 3260–3267 (2011). IEEE

  6. Mohanan, M.G., Salgoankar, A.: A survey of robotic motion planning in dynamic environments. Robotics and Autonomous Systems 100, 171–185 (2018). https://doi.org/10.1016/j.robot.2017.10.011

    Article  Google Scholar 

  7. Kushleyev, A., Likhachev, M.: Time-bounded lattice for efficient planning in dynamic environments. In: 2009 IEEE International Conference on Robotics and Automation, pp. 1662–1668 (2009)

  8. Cai, K., Wang, C., Cheng, J., De Silva, C.W., Meng, M.Q.-H.: Mobile robot path planning in dynamic environments: A survey. arXiv e-prints, 2006–14195 (2020) https://arxiv.org/abs/2006.14195arXiv:2006.14195 [cs.RO]

  9. Hasan, A.H., Mosa, A.M.: Multi-robot path planning based on max-min ant colony optimization and d* algorithms in a dynamic environment. In: 2018 International Conference on Advanced Science and Engineering (ICOASE), pp. 110–115 (2018)

  10. Ziebart, B.D., Ratliff, N., Gallagher, G., Mertz, C., Peterson, K., Bagnell, J.A., Hebert, M., Dey, A.K., Srinivasa, S.: Planning-based prediction for pedestrians. In: 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 3931–3936 (2009). IEEE

  11. Kretzschmar, H., Spies, M., Sprunk, C., Burgard, W.: Socially compliant mobile robot navigation via inverse reinforcement learning. The International Journal of Robotics Research 35(11), 1289–1307 (2016)

    Article  Google Scholar 

  12. Kirby, R., Simmons, R., Forlizzi, J.: Companion: A constraint-optimizing method for person-acceptable navigation. In: RO-MAN 2009-The 18th IEEE International Symposium on Robot and Human Interactive Communication, pp. 607–612 (2009). IEEE

  13. Sisbot, E.A., Marin-Urias, L.F., Alami, R., Simeon, T.: A human aware mobile robot motion planner. IEEE Transactions on Robotics 23(5), 874–883 (2007)

    Article  Google Scholar 

  14. Ferrer, G., Garrell, A., Sanfeliu, A.: Social-aware robot navigation in urban environments. In: 2013 European Conference on Mobile Robots, pp. 331–336 (2013). IEEE

  15. Shiomi, M., Zanlungo, F., Hayashi, K., Kanda, T.: Towards a socially acceptable collision avoidance for a mobile robot navigating among pedestrians using a pedestrian model. International Journal of Social Robotics 6(3), 443–455 (2014)

    Article  Google Scholar 

  16. Atzmon, D., Stern, R., Felner, A., Sturtevant, N.R., Koenig, S.: Probabilistic robust multi-agent path finding. In: Proceedings of the International Conference on Automated Planning and Scheduling, vol. 30, pp. 29–37 (2020)

  17. Santos, V.G., Chaimowicz, L.: Hierarchical congestion control for robotic swarms. In: 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 4372–4377 (2011). IEEE

  18. Toutouh, J., Alba, E.: A swarm algorithm for collaborative traffic in vehicular networks. Vehicular Comms. 12, 127–137 (2018)

    Article  Google Scholar 

  19. Antoniou, P., Pitsillides, A., Blackwell, T., Engelbrecht, A., Michael, L.: Congestion control in wireless sensor networks based on bird flocking behavior. Computer Networks 57(5), 1167–1191 (2013). https://doi.org/10.1016/j.comnet.2012.12.008

    Article  Google Scholar 

  20. Tatomir, B., Rothkrantz, L.: Hierarchical routing in traffic using swarm-intelligence. In: 2006 IEEE Intelligent Transportation Systems Conference, pp. 230–235 (2006). https://doi.org/10.1109/ITSC.2006.1706747

  21. Gunes, M., Sorges, U., Bouazizi, I.: Ara-the ant-colony based routing algorithm for manets. In: Proceedings. International Conference on Parallel Processing Workshop, pp. 79–85 (2002). IEEE

  22. Di Caro, G., Ducatelle, F., Gambardella, L.M.: Anthocnet: an adaptive nature-inspired algorithm for routing in mobile ad hoc networks. European Transactions on Telecommunications 16(5), 443–455 (2005)

    Article  Google Scholar 

  23. Street, C., Pütz, S., Mühlig, M., Hawes, N., Lacerda, B.: Congestion-aware policy synthesis for multirobot systems. IEEE Transactions on Robotics (2021)

  24. Stump, E., Michael, N.: Multi-robot persistent surveillance planning as a vehicle routing problem. In: IEEE International Conference on Automation Science and Engineering, pp. 569–575 (2011)

  25. Leahy, K., Zhou, D., Vasile, C.-I., Oikonomopoulos, K., Schwager, M., Belta, C.: Persistent surveillance for unmanned aerial vehicles subject to charging and temporal logic constraints. Auton. Robots 40(8), 1363–1378 (2016). https://doi.org/10.1007/s10514-015-9519-z

  26. Kusnur, T., Mukherjee, S., Saxena, D.M., Fukami, T., Koyama, T., Salzman, O., Likhachev, M.: A Planning Framework for Persistent, Multi-UAV Coverage with Global Deconfliction. In: Proceedings of the 12th Conference on Field and Service Robotics (2019)

  27. Thakur, D., Likhachev, M., Keller, J., Kumar, V., Dobrokhodov, V., Jones, K., Wurz, J., Kaminer, I.: Planning for Opportunistic Surveillance with Multiple Robots. In: IEEE/RSJ International Conference on Intelligent Robots and Systems (2013)

  28. Van den Berg, J., Lin, M., Manocha, D.: Reciprocal velocity obstacles for real-time multi-agent navigation. In: 2008 IEEE International Conference on Robotics and Automation, pp. 1928–1935 (2008). IEEE

  29. Mavrogiannis, C.I., Knepper, R.A.: Multi-agent path topology in support of socially competent navigation planning. The International Journal of Robotics Research 38(2–3), 338–356 (2019)

    Article  Google Scholar 

  30. Mavrogiannis, C., Balasubramanian, K., Poddar, S., Gandra, A., Srinivasa, S.S.: Winding through: Crowd navigation via topological invariance. IEEE Robotics and Automation Letters 8(1), 121–128 (2022)

    Article  Google Scholar 

  31. LaValle, S.M.: Robot motion planning: A game-theoretic foundation. Algorithmica 26(3), 430–465 (2000)

    Article  MathSciNet  MATH  Google Scholar 

  32. He, C., Wan, Y., Gu, Y., Lewis, F.L.: Integral reinforcement learning-based multi-robot minimum time-energy path planning subject to collision avoidance and unknown environmental disturbances. IEEE Control Systems Letters 5(3), 983–988 (2020)

    Article  MathSciNet  Google Scholar 

  33. Bhattacharya, S., Likhachev, M., Kumar, V.: Topological constraints in search-based robot path planning. Autonomous Robots, 1–18 (2012). DOI: https://doi.org/10.1007/s10514-012-9304-1

  34. Bhattacharya, S., Ghrist, R.: Path homotopy invariants and their application to optimal trajectory planning. Annals of Mathematics and Artificial Intelligence 84(3–4), 139–160 (2018)

    Article  MathSciNet  MATH  Google Scholar 

  35. Wang, X., Bhattacharya, S.: A topological approach to workspace and motion planning for a cable-controlled robot in cluttered environments. IEEE Robotics and Automation Letters 3(3), 2600–2607 (2018). https://doi.org/10.1109/LRA.2018.2817684

    Article  Google Scholar 

  36. Govindarajan, V., Bhattacharya, S., Kumar, V.: Human-robot collaborative topological exploration for search & rescue applications. In: Intl. Symp. on Distributed Autonomous Robotic Sysystems (2014)

  37. Bhattacharya, S., Ghrist, R., Kumar, V.: Persistent homology for path planning in uncertain environments. IEEE Transactions on Robotics (T-RO) 31(3), 578–590 (2015)

  38. Wu, W., Bhattacharya, S., Prorok, A.: Multi-robot path deconfliction through prioritization by path prospects. In: Proceedings of IEEE Intl. Conference on Robotics and Automation (ICRA) (2020)

  39. Bhattacharya, S., Kim, S., Heidarsson, H., Sukhatme, G., Kumar, V.: A topological approach to using cables to separate and manipulate sets of objects. International Journal of Robotics Research 34(6), 799–815 (2015). https://doi.org/10.1177/0278364914562236

    Article  Google Scholar 

  40. Choset, H., Lynch, K.M., Hutchinson, S., Kantor, G.A., Burgard, W., Kavraki, L.E., Thrun, S.: Principles of Robot Motion: Theory, Algorithms, and Implementations. Theory, Algorithms, and Implementations. MIT Press, Cambridge, MA, Principles of Robot Motion (2005)

    MATH  Google Scholar 

  41. Boyd, S., Vandenberghe, L.: Convex Optimization. Cambridge University Press, Cambridge, UK (2004)

    Book  MATH  Google Scholar 

  42. Fox, D., Burgard, W., Thrun, S.: Active markov localization for mobile robots. Robotics and Autonomous Systems 25(3), 195–207 (1998). Autonomous Mobile Robots

  43. Zhang, L., Prorok, A., Bhattacharya, S.: Pursuer assignment and control strategies in multi-agent pursuit-evasion under uncertainties. Frontiers in Robotics and AI 8, 262 (2021). https://doi.org/10.3389/frobt.2021.691637

  44. d’Andrea-Novel, B., Bastin, G., Campion, G.: Dynamic feedback linearization of nonholonomic wheeled mobile robots. In: Proceedings 1992 IEEE International Conference on Robotics and Automation, pp. 2527–2528 (1992). IEEE Computer Society

  45. Jia, Y., Yan, X., Xu, Y.: A survey of simultaneous localization and mapping for robot. In: 2019 IEEE 4th Advanced Information Technology, Electronic and Automation Control Conference (IAEAC), vol. 1, pp. 857–861 (2019). IEEE

  46. Daniel, K., Nash, A., Koenig, S., Felner, A.: Theta*: Any-angle path planning on grids. Journal of Artificial Intelligence Research 39, 533–579 (2010)

  47. Bhattacharya, S.: Towards optimal path computation in a simplicial complex. The International Journal of Robotics Research 38(8), 981–1009 (2019)

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Acknowledgements

This material is based upon work supported by the National Science Foundation under Grant No. CCF-2144246.

Funding

This work was partially supported by the National Science Foundation (Grant No. CCF-2144246).

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Authors and Affiliations

  1. Department of Mechanical Engineering and Mechanic, Lehigh University, 19 Memorial Drive West, Bethlehem, 18015, PA, USA

    Xiaolong Wang, Alp Sahin & Subhrajit Bhattacharya

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  1. Xiaolong Wang
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Contributions

Xiaolong Wang was responsible for implementation of the proposed algorithms for topological planning, stochastic path choice and fast replanning, and helped run the simulations and experiments. Alp Sahin was responsible for implementation of the proposed controllers for fast replanning and local collision avoidance, and helped run the simulations and experiments. Subhrajit Bhattacharya was responsible for inception of the main ideas behind the research effort, development of the proposed algorithms, development of the mathematical formulations/models, overseeing the implementation of the algorithms, and coordination of the research activities. All authors contributed equally in writing the paper.

Corresponding author

Correspondence to Subhrajit Bhattacharya.

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Cite this article

Wang, X., Sahin, A. & Bhattacharya, S. Coordination-free Multi-robot Path Planning for Congestion Reduction Using Topological Reasoning. J Intell Robot Syst 108, 50 (2023). https://doi.org/10.1007/s10846-023-01878-3

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  • Received: 17 October 2022

  • Accepted: 29 April 2023

  • Published: 10 July 2023

  • DOI: https://doi.org/10.1007/s10846-023-01878-3

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Keywords

  • Multi-Robot motion planning
  • Topological path planning
  • Privacy-aware planning
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