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Routing-based navigation of dense mobile robots

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Abstract

In the future, many teams of robots will navigate in home or office environments, similar to dense crowds operating currently in different scenarios. The paper aims to route a large number of robots so as to avoid build-up of congestions, similar to the problem of route planning of traffic systems. In this paper, first probabilistic roadmap approach is used to get a roadmap for online motion planning of robots. A graph search-based technique is used for motion planning. In the literature, typically the search algorithms consider only the static obstacles during this stage, which results in too many robots being scheduled on popular/shorter routes. The algorithm used here therefore penalizes roadmap edges that lie in regions with large robot densities so as to judiciously route the robots. This planning is done continuously to adapt the path to changing robotic densities. The search returns a deliberative trajectory to act as a guide for the navigation of the robot. A point at a distant of the deliberative path becomes the immediate goal of the reactive system. A ‘centre of area’-based reactive navigation technique is used to reactively avoid robots and other dynamic obstacles. In order to avoid two robots blocking each other and causing a deadlock, a deadlock avoidance scheme is designed that detects deadlocks, makes the robots wait for a random time and then allows them to make a few random steps. Experimental results show efficient navigation of a large number of robots. Further, routing results in effectively managing the robot densities so as to enable an efficient navigation.

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References

  1. 1.

    Choset H, Lynch KM, Hutchinson S, Kantor GA, Burgard W, Kavraki LE, Thrun S (2005) Principles of robot motion: theory, algorithms, and implementations. MIT Press, Cambridge

  2. 2.

    Tiwari R, Shukla A, Kala R (2013) Intelligent planning for mobile robotics. IGI Global Publishers, Hershey

  3. 3.

    Chand P, Carnegie DA (2012) A two-tiered global path planning strategy for limited memory mobile robots. Robot Auton Syst 60(3):309–321

  4. 4.

    Kala R, Shukla A, Tiwari R (2010) Fusion of probabilistic \(\text{ A }^{*}\) algorithm and fuzzy inference system for robotic path planning. Artif Intel Rev 33(4):275–306

  5. 5.

    Kavraki LE, Svestka P, Latombe JC, Overmars MH (1996) Probabilistic roadmaps for path planning in high-dimensional configuration spaces. IEEE Trans Robot Autom 12(4):566–580

  6. 6.

    Bohlin R, Kavraki LE (2000) Path planning using lazy PRM. In: Proceedings of the IEEE international conference on robotics and automation, pp 521–528

  7. 7.

    Claes R, Holvoet T, Weyns D (2011) A decentralized approach for anticipatory vehicle routing using delegate multiagent systems. IEEE Trans Intell Transp Syst 12(2):64–373

  8. 8.

    Kala R, Warwick K (2015) Congestion avoidance in city traffic. J Adv Transp 49(4):581–595

  9. 9.

    Spears WM, Spears DF, Hamann JC, Heil R (2004) Distributed, physics-based control of swarms of vehicles. Auton Robot 17(2–3):137–162

  10. 10.

    Pelechano N, Allbeck JM, Badler NI (2007) Controlling individual agents in high-density crowd simulation, In: Proceedings of the 2007 ACM SIGGRAPH/Eurographics symposium on Computer animation, pp 99–108

  11. 11.

    Musse SR, Thalmann D (2001) Hierarchical model for real time simulation of virtual human crowds. IEEE Trans Virtual Comput Graph 7(2):152–164

  12. 12.

    Curtis S, Best AP, Manocha D (2016) Menge: a modular framework for simulating crowd movement. Collect Dyn 1(A1):1–40

  13. 13.

    Gu Q, Deng Z (2011) Context-aware motion diversification for crowd simulation. IEEE Comput Graph Appl 31(5):54–65

  14. 14.

    Kountouriotis V, Thomopoulos SCA, Papelis Y (2014) An agent-based crowd behaviour model for real time crowd behavior simulation. Pattern Recogn Lett 44:30–38

  15. 15.

    Han Y, Liu H, Moore P (2017) Extended route choice model based on available evacuation route set and its application in crowd evacuation simulation. Simul Model Pract Theory 75:1–16

  16. 16.

    Golas A, Narain R, Curtis S, Lin MC (2014) Hybrid long-range collision avoidance for crowd simulation. IEEE Trans Vis Comput Graph 20(7):1022–1034

  17. 17.

    Cowlagi RV, Tsiotras P (2012) Hierarchical motion planning with dynamical feasibility guarantees for mobile robotic vehicles. IEEE Trans Robot 28(2):379–395

  18. 18.

    Sgorbissa A, Zaccaria R (2012) Planning and obstacle avoidance in mobile robotics. Robot Auton Syst 60(4):628–638

  19. 19.

    Chang YC, Yamamoto Y (2009) Path planning of wheeled mobile robot with simultaneous free space locating capability. Intell Serv Robot 2(1):9–22

  20. 20.

    Yao Z, Gupta K (2011) Distributed roadmaps for robot navigation in sensor networks. IEEE Trans Robot 27(5):997–1004

  21. 21.

    Clark CM (2005) Probabilistic road map sampling strategies for multi-robot motion planning. Robot Auton Syst 53:244–264

  22. 22.

    Chai R, Su J (2013) Motion planning for multi-robot coordination. In: 13th IFAC symposium on large scale complex systems: theory and applications, Shanghai, China, pp 129–134

  23. 23.

    Alvarez-Sanchez JR, de la Paz Lopez F, Troncoso JMC, de Santos Sierra D (2010) Reactive navigation in real environments using partial center of area method. Robot Auton Syst 58(12):1231–1237

  24. 24.

    Sezer V, Gokasan M (2012) A novel obstacle avoidance algorithm: follow the gap method. Robot Auton Syst 60(9):1123–1134

  25. 25.

    Kim Y, Kwon SJ (2015) A heuristic obstacle avoidance algorithm using vanishing point and obstacle angle. Intell Serv Robot 8(3):175–183

  26. 26.

    Fiorini P, Shiller Z (1998) Motion planning in dynamic environments using velocity obstacles. Int J Robot Res 17(7):760–772

  27. 27.

    Snape J, van den Berg J, Guy SJ, Manocha D (2011) The hybrid reciprocal velocity obstacle. IEEE Trans Robot 27(4):696–706

  28. 28.

    Rashid AT, Ali AA, Frasca M, Fortuna L (2012) Multi-robot collision-free navigation based on reciprocal orientation. Robot Auton Syst 60(10):1221–1230

  29. 29.

    Karagoz CS, Bozma HI, Koditschek DE (2014) Coordinated navigation of multiple independent disk-shaped robots. IEEE Trans Robot 30(6):1289–1304

  30. 30.

    Zhong J, Cai W, Lees M, Luo L (2017) Automatic model construction for the behaviour of human crowds. Appl Soft Comput. https://doi.org/10.1016/j.asoc.2017.03.020

  31. 31.

    Patil S, van den Berg J, Curtis S, Lin MC, Manocha D (2011) Directing crowd simulations using navigation fields. IEEE Trans Vis Comput Graph 17(2):244–254

  32. 32.

    Hsu D, Sanchez-Ante G, Sun Z (2005) Hybrid PRM sampling with a cost-sensitive adaptive strategy. In: Proceedings of the 2005 IEEE international conference on robotics and automation, Barcelona, Spain, pp 3874–3880

  33. 33.

    Rodriguez S, Thomas S, Pearce R, Amato NM (2008) RESAMPL: a region-sensitive adaptive motion planner. In: Algorithmic Foundation of Robotics VII, Springer Tracts in Advanced Robotics. Springer, Berlin, pp 285–300

  34. 34.

    Morales M, Tapia L, Pearce R, Rodriguez S, Amat NM (2005) A machine learning approach for feature-sensitive motion planning. In: Algorithmic Foundations of Robotics VI, Springer Tracts in Advanced Robotics, Springer, Berlin, vol 17, pp 361–376

  35. 35.

    Kala R (2016) Homotopy conscious roadmap construction by fast sampling of narrow corridors. Appl Intell 45(4):1089–1102

  36. 36.

    Hsu D, Jiang T, Reif J, Sun Z (2003) The bridge test for sampling narrow passages with probabilistic roadmap planners. In: Proceedings of the 2003 IEEE international conference on robotics and automation, vol 3, pp 4420–4426

  37. 37.

    Amato NM, Bayazit OB, Dale LK, Jones C, Vallejo D (1998) Obprm: an obstacle-based prm for 3d workspaces. In: Agarwal P, Kavraki LE, Mason M (eds) Robotics: the algorithmic perspective. A.K. Peters, Natick, pp 155–168

  38. 38.

    La Valle SM, Kuffner JJ (2001) Randomized kinodynamic planning. Int J Robot Res 20(5):378–400

  39. 39.

    Kuffner JJ, LaValle SM (2000) RRT-connect: an efficient approach to single-query path planning. In: Proceedings of the IEEE international conference on robotics and automation, pp 995–1001

  40. 40.

    Russell S, Norvig P (2010) Artificial intelligence: a modern approach, 3rd edn. Pearson, Harlow

  41. 41.

    Helbing D, Molnar P (1995) Social force model for pedestrians dynamics. Phy Rev E 51(5):42–82

  42. 42.

    Martinez-Garcia E, Torres-Cordoba R (2012) Exponential fields formulation for WMR navigation. J Appl Bion Biomech 9(4):375–397

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Author information

Correspondence to Rahul Kala.

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Supplementary material 1 (mp4 6503 KB)

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Cite this article

Kala, R. Routing-based navigation of dense mobile robots. Intel Serv Robotics 11, 25–39 (2018) doi:10.1007/s11370-017-0243-8

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Keywords

  • Motion planning
  • Navigation
  • Swarm robotics
  • Multi-agent systems
  • Routing
  • Probabilistic roadmap