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Multi-vehicle planning using RRT-connect

  • Research Article
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Paladyn

Abstract

The problem of planning multiple vehicles deals with the design of an e ective algorithm that can cause multiple autonomous vehicles on the road to communicate and generate a collaborative optimal travel plan. Our modelling of the problem considers vehicles to vary greatly in terms of both size and speed, which makes it sub-optimal to have a faster vehicle follow a slower vehicle or for vehicles to drive with predefined speed lanes. It is essential to have a fast planning algorithm whilst still being probabilistically complete. The Rapidly Exploring Random Trees (RRT) algorithm developed and reported on here uses a problem specific coordination axis, a local optimization algorithm, priority based coordination, and a module for deciding travel speeds. Vehicles are assumed to remain in their current relative position laterally on the road unless otherwise instructed. Experimental results presented here show regular driving behaviours, namely vehicle following, overtaking, and complex obstacle avoidance. The ability to showcase complex behaviours in the absence of speed lanes is characteristic of the solution developed.

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References

  1. M. Buehler, K. Iagnemma, S. Singh, The 2005 DARPA grand challenge: the great robot race, Springer, Berlin, Heidelberg, 2007.

    Book  Google Scholar 

  2. G. Seetharaman, A. Lakhotia, E. P. Blasch, Unmanned Vehicles Come of Age: The DARPA Grand Challenge, Computer, 39(12) (2006), 26–29.

    Article  Google Scholar 

  3. S. Tsugawa, Inter-vehicle communications and their applications to intelligent vehicles: an overview, Proceedings of the IEEE Intelligent Vehicle Symposium Vol. 2, 2002, 564–569.

    Google Scholar 

  4. M. Montemerlo, J. Becker, S. Bhat, H. Dahlkamp, D. Dolgov, S. Ettinger, D. Haehnel, T. Hilden, G. Ho mann, B. Huhnke, D. Johnston, S. Klumpp, D. Langer, A. Levandowski, J. Levinson, J. Marcil, D. Orenstein, J. Paefgen, I. Penny, A. Petrovskaya, M. Pflueger, G. Stanek, D. Stavens, A. Vogt, S. Thrun, Junior: The Stanford entry in the Urban Challenge, Journal of Field Robotics, 25(9)(2008), pp 569–597, 2008.

    Article  Google Scholar 

  5. S. A. Nobe, F. Y. Wang, An overview of recent developments in automated lateral and longitudinal vehicle controls, Proceedings of the 2001 IEEE International Conference on Systems, Man, and Cybernetics vol.5, 2001, 3447–3452.

    Google Scholar 

  6. L. Vanajakshi, S. C. Subramanian, R. Sivanandan, Travel time prediction under heterogeneous tra c conditions using global positioning system data from buses, IET Intelligent Transport Systems, 3(1) (2009), 1–9.

    Article  Google Scholar 

  7. J. R. Alvarez-Sanchez, F. de la Paz Lopez, J. M. C. Troncoso, D. de Santos Sierra, Reactive navigation in real environments using partial center of area method, Robotics and Autonomous Systems, 58(12) (2010), 1231–1237.

    Article  Google Scholar 

  8. R. Kala, A. Shukla, R. Tiwari Fusion of probabilistic A* algorithm and fuzzy inference system for robotic path planning, Artificial Intelligence Review, 33(4) (2010), 275–306.

    Article  Google Scholar 

  9. O. Khatib, Real-Time Obstacle Avoidance for Manipulators and Mobile Robots, The International Journal of Robotics Research, 5(1986), 90–98.

    Article  Google Scholar 

  10. R. Kala, A. Shukla, R. Tiwari Robotic Path Planning using Evolutionary Momentum based Exploration, Journal of Experimental and Theoretical Artificial Intelligence, 23(4) (2011a), 469–495.

    Article  Google Scholar 

  11. R. Kala, A. Shukla, R. Tiwari, Dynamic Environment Robot Path Planning using Hierarchical Evolutionary Algorithms, Cybernetics and Systems, 41(6) (2010), 435–454.

    Article  Google Scholar 

  12. R. Kala, A. Shukla, R. Tiwari, Robotic path planning in static environment using hierarchical multi-neuron heuristic search and probability based fitness, Neurocomputing 74(14–15) (2011b), 2314–2335.

    Article  Google Scholar 

  13. J. J. Ku ner, S. M. LaValle, RRT-connect: An e cient approach to single-query path planning, Proceedings of the IEEE International Conference on Robotics and Automation vol. 2, 2000, 995–1001.

    Google Scholar 

  14. S. M. LaValle, J. J. Kuffner, Randomized kinodynamic planning, Proceedings of the IEEE International Conference on Robotics and Automation, 1999, pp. 473–479.

  15. Y. Kuwata, S. Karaman, J. Teo, E. Frazzoli, J. P. How, G. Fiore, Real-Time Motion Planning With Applications to Autonomous Urban Driving, IEEE Transactions on Control Systems Technology, 17(5) (2009), 1105–1118.

    Article  Google Scholar 

  16. J. Leonard, J. How, S. Teller, M. Berger, S. Campbell, G. Fiore, L. Fletcher, E. Frazzoli, A. Huang, S. Karaman, O. Koch, Y. Kuwata, D. Moore, E. Olson, S. Peters, J. Teo, R. Truax, M. Walter, D. Barrett, A. Epstein, K. Maheloni, K. Moyer, T. Jones, R. Buckley, M. Antone, R. Galejs, S. Krishnamurthy, J. Williams, A perception driven autonomous urban vehicle, Journal of Field Robotics, 25(10) (2008), 727–774.

    Article  Google Scholar 

  17. S. Chakravorty, S. Kumar, Generalized sampling based motion planners with application to nonholonomic systems, Proceedings of the IEEE International Conference on Systems, Man and Cybernetics, 2009, 4077–4082.

  18. R. Kala, K. Warwick, Planning of Multiple Autonomous Vehicles using RRT, Proceedings of the 10th IEEE International Conference on Cybernetic Intelligent Systems, Docklands, London, 2011.

  19. E. K. Xidias, P. N. Azariadis, Mission design for a group of autonomous guided vehicles, Robotics and Autonomous Systems, 59(1) (2011), 34–43.

    Article  Google Scholar 

  20. C. de Boor, A Practical Guide to Splines, Springer, Berlin-Heidelberg, 1978.

    Book  MATH  Google Scholar 

  21. M. Bennewitz, W. Burgard, S. Thrun, Optimizing schedules for prioritized path planning of multi-robot systems, Proceedings of the 2001 IEEE International Conference on Robotics and Automation, Seoul, Korea, 2001, 271–276.

  22. M. Bennewitz, W. Burgard, S. Thrun, Finding and optimizing solvable priority schemes for decoupled path planning techniques for teams of mobile robots, Robotics and Autonomous Sysems, 41(2–3) (2002), 89–99.

    Article  Google Scholar 

  23. T. H. Cormen, C. E. Leiserson, R. L. Rivest, C. Stein, Chapter 11: Hash Tables, Introduction to Algorithms, Second Edition, MIT Press, Massacusettes, 2001, 221–245.

    Google Scholar 

  24. B. Raveh, A. Enosh, D. Halperin, A Little More, a Lot Better: Improving Path Quality by a Path-Merging Algorithm, IEEE Transactions on Robotics, 27(2) (2011), 365–371.

    Article  Google Scholar 

  25. R. Kala, Multi-Robot Path Planning using Co-Evolutionary Genetic Programming, Expert Systems With Applications, 39(3) (2012), 3817–3831.

    Article  Google Scholar 

  26. J. Bruce, M. Veloso, Real-Time Multi-Robot Motion Planning with Safe Dynamics, In: Multi-Robot Systems: From Swarms to Intelligent Automata Vol. 3, Springer, placeCityHeidelberg, 2005, 159–170.

    Chapter  Google Scholar 

  27. L. Zhang, D. Manocha, An e cient retraction-based RRT planner, IEEE International Conference on Robotics and Automation, 2008 2008, 3743–3750.

  28. J. Sewall, J. van den Berg, M. C. Lin, D. Manocha, Virtualized Traffic: Reconstructing Tra c Flows from Discrete Spatio-temporal Data, IEEE Transactions on Visualization and Computer Graphics, 17(1) (2011), 26–37.

    Article  Google Scholar 

  29. R. Schubert, K. Schulze, G. Wanielik, Situation Assessment for Automatic Lane-Change Maneuvers, IEEE Transactions on Intelligent Trasportation Systems, 11(3) (2010), 607–616.

    Article  Google Scholar 

  30. A. Furda, L. Vlacic, Enabling Safe Autonomous Driving in Real-World City Tra c Using Multiple Criteria Decision Making, IEEE Intelligent Trasportation Systems Magazine, 3(1) (2011), 4–17.

    Article  Google Scholar 

  31. J. E. Naranjo, C. González, R. García, T. de Pedro, Lane-Change Fuzzy Control in Autonomous Vehicles for the Overtaking Maneuver, IEEE Transactions on Intelligent Trasportation Systems, 9(3) (2008), 438–450.

    Article  Google Scholar 

  32. G. Hegeman, A. Tapani, S. Hoogendoorn, Overtaking assistant assessment using tra c simulation, Transportation Research Part C, 17(6) (2009), 617–630.

    Article  Google Scholar 

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Authors and Affiliations

  1. School of Cybernetics, School of Systems Engineering, University of Reading, Whiteknights, Reading, Berkshire, UK

    Rahul Kala & Kevin Warwick

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  1. Rahul Kala
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  2. Kevin Warwick
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Correspondence to Rahul Kala.

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Extended version of the paper originally published in CIS2011

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Kala, R., Warwick, K. Multi-vehicle planning using RRT-connect. Paladyn 2, 134–144 (2011). https://doi.org/10.2478/s13230-012-0004-5

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  • DOI: https://doi.org/10.2478/s13230-012-0004-5

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