Multi-vehicle planning using RRT-connect

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Kala, R. and Warwick, K. (2011) Multi-vehicle planning using RRT-connect. Paladyn. Journal of Behavioral Robotics, 2 (3). pp. 134-144. ISSN 2081-4836 doi: 10.2478/s13230-012-0004-5

Abstract/Summary

The problem of planning multiple vehicles deals with the design of an effective 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 suboptimal 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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Item Type	Article
URI	https://reading-clone.eprints-hosting.org/id/eprint/32262
Item Type	Article
Refereed	Yes
Divisions	Science
Uncontrolled Keywords	Autonomous vehicles, rapidly exploring random trees, RRT-Connect, multi-robot path planning, coordination, robocars, planning, intelligent vehicles.
Publisher	Versita; Springer
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Deposit Details

Date Deposited:	24 Apr 2013 07:14	Date item deposited into CentAUR
Last Modified:	20 Oct 2024 05:22	Date item last modified

References

[1] M. Buehler, K. Iagnemma, S. Singh, The 2005 DARPA grand challenge: the great robot race, Springer, Berlin, Heidelberg, 2007. [2] G. Seetharaman, A. Lakhotia, E. P. Blasch, Unmanned Vehicles Come of Age: The DARPA Grand Challenge, Computer, 39(12) (2006), 26-29. [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. [4] M. Montemerlo, J. Becker, S. Bhat, H. Dahlkamp, D. Dolgov, S. Ettinger, D. Haehnel, T. Hilden, G. Hoffmann, 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. [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. [6] L. Vanajakshi, S. C. Subramanian, R. Sivanandan, Travel time prediction under heterogeneous traffic conditions using global positioning system data from buses, IET Intelligent Transport Systems, 3(1) (2009), 1-9. [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. [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. [9] O. Khatib, Real-Time Obstacle Avoidance for Manipulators and Mobile Robots, The International Journal of Robotics Research, 5(1986), 90-98. [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. [11] R. Kala, A. Shukla, R. Tiwari, Dynamic Environment Robot Path Planning using Hierarchical Evolutionary Algorithms, Cybernetics and Systems, 41(6) (2010), 435-454. [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. [13] J. J. Kuffner, S. M. LaValle, RRT-connect: An efficient approach to single-query path planning, Proceedings of the IEEE International Conference on Robotics and Automation vol. 2, 2000, 995-1001. [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. [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. [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. [20] C. de Boor, A Practical Guide to Splines, Springer, Berlin-Heidelberg, 1978. [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. [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. [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. [25] R. Kala, Multi-Robot Path Planning using Co-Evolutionary Genetic Programming, Expert Systems With Applications, 39(3) (2012), 3817-3831. [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, Heidelberg, 2005, 159-170. [27] L. Zhang, D. Manocha, An efficient 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 Traffic Flows from Discrete Spatio-temporal Data, IEEE Transactions on Visualization and Computer Graphics, 17(1) (2011), 26-37. [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. [30] A. Furda, L. Vlacic, Enabling Safe Autonomous Driving in Real-World City Traffic Using Multiple Criteria Decision Making, IEEE Intelligent Trasportation Systems Magazine, 3(1) (2011), 4-17. [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. [32] G. Hegeman, A. Tapani, S. Hoogendoorn, Overtaking assistant assessment using traffic simulation, Transportation Research Part C, 17(6) (2009), 617–630.

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