Abstract
We solve the problem of robot path planning using Dynamic Programming (DP) designed to perform well in case of a sudden path blockage. A conventional DP algorithm works well for real time scenarios only when the update frequency is high i.e. changes can be readily propagated. In case updates are costly, for a sudden blockage the robot continues moving along the wrong path or stands stationary. We propose a modified DP that has nodes with additional processing (called accelerating nodes) to enable different segments of the map to become informed about the blockage rapidly. We further quickly compute an alternative path in case of a blockage. Experimental results verify that usage of accelerating nodes makes the robot follow optimal paths in dynamic environments.
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A. R. Willms, S. X. Yang, An Efficient Dynamic System for Real-Time Robot-Path Planning, IEEE Transactions on Systems Man and Cybernetics — Part B Cybernetics, 36(4) (2006), 755–766
S. X. Yang, M. Meng, An efficient neural network approach to dynamic robot motion planning, Neural Networks, 13(2)(2000), 143–148.
A. Zelinsky, Using path transforms to guide the search for findpath in 2d, International Journal of Robotic Research, 13(4)(1994), 315–325
D. V. Lebedev, J. J. Steil, H. J. Ritter, The dynamic wave expansion neural network model for robot motion planning in time-varying environments, Neural Networks, 18(3), 267–285.
A. Shukla, R. Tiwari, R. Kala, Mobile Robot Navigation Control in Moving Obstacle Environment using A* Algorithm, In Proceedings of the International Conference on Artificial Neural Networks in Engineering Vol. 18, ASME Publications, 2008, 113–120.
S. Kambhampati, L. Davis, Multiresolution path planning for mobile robots, IEEE Journal of Robotics and Automation, 2(3)(1986), 135–145.
J. Y. Hwang, J. S. Kim, S. S. Lim, K. H. Park, A Fast Path Planning by Path Graph Optimization, IEEE Transaction on Systems, Man, and Cybernetics—Part A: Systems and Humans, 33(1)(2003), 121–128.
A. Yahja, A. Stentz, S. Singh, B. L. Brumitt, Framed-quadtree path planning for mobile robots operating in sparse environments, In Proceedings of the 1998 IEEE International Conference on Robotics and Automation, 1998, 650–655.
C. Urdiales, A. Bantlera, F. Arrebola, F. Sandoval, Multi-level path planning algorithm for autonomous robots, IEEE Electronic Letters, 34(2) (1998), 223–224.
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) (2011), 2314–2335.
A. Shukla, R. Kala, Multi-Neuron Heuristic Search, International Journal of Computer Science and Network Security, 8(6) (2008), 344–350.
R. Kala, A. Shukla, R. Tiwari, Robotic Path Planning using Multi-Neuron Heuristic Search, In Proceedings of the 4th International Conference on Computer Sciences and Convergence Information Technology, 2009, 1318–1323.
Y. Lu, X. Huo, O. Arslan, P. Tsiotras, Incremental Multi-Scale Search Algorithm for Dynamic Path Planning With Low Worst-Case Complexity, IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, 41(6)(2011), 1556–1570.
S. Koenig, M. Likhachev, and D. Furcy, Lifelong planning A*, Artificial Intelligence, 155(1–2) (2004), 93–146.
A. Stentz, Optimal and efficient path planning for partially-known environments, In Proceedings of the 1994 IEEE International Conference on Robotics and Automation, 1994, 3310–3317.
D. Cagigas, J. Abascal, A Hierarchical Extension of the D* Algorithm, Journal of Intelligent and Robotic Systems, 42(4)(2005), 393–413.
J. J. Kuffner, S. M. LaValle, RRT-connect: An efficient approach to single-query path planning, In Proceedings of the IEEE International Conference on Robotics and Automation vol. 2, 2000, 995–1001.
S. M. LaValle, J. J. Kuffner, Randomized kinodynamic planning, In Proceedings of the IEEE International Conference on Robotics and Automation, 1999, 473–479.
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.
L. Zhang, D. Manocha, An efficient retraction-based RRT planner, In Proceedings of the IEEE International Conference on Robotics and Automation, 2008, 3743–3750.
L. E. Kavraki, M. N. Kolountzakis, J. C. Latombe, Analysis of probabilistic roadmaps for path planning, IEEE Transactions on Robotics and Automation, 14(1) (1998), 166–171.
L. E. Kavraki, P. Svestka, J. C. Latombe, M. H. Overmars, Probabilistic roadmaps for path planning in high-dimensional configuration spaces, IEEE Transactions on Robotics and Automation, 12(4)(1996), 566–580.
R. Gayle, A. Sud, E. Andersen, S. J. Guy, M. C. Lin, D. Manocha, Interactive Navigation of Heterogeneous Agents Using Adaptive Roadmaps, IEEE Transactions on Visualization and Computer Graphics, 15(1)(2009), 34–48.
R. Bohlin, L. E. Kavraki, Path Planning Using Lazy PRM, In Proceedings of the 2000 IEEE International Conference on Robotics and Automation, 2000, 521–528.
C. M. Clark, Probabilistic Road Map sampling strategies for multi-robot motion planning, Robotics and Autonomous Systems, 53(2005), 244–264.
K. J. O’ Hara, D. B. Walker, T. R. Balch, Physical Path Planning Using a Pervasive Embedded Network, IEEE Trans. Robotics, 24(3)(2008), 741–746.
Z. Yao, K. Gupta. Distributed Roadmaps for Robot Navigation in Sensor Networks, IEEE Transactions on Robotics, 27(5)(2011), 997–1004.
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Kala, R., Shukla, A. & Tiwari, R. Robot path planning using dynamic programming with accelerating nodes. Paladyn 3, 23–34 (2012). https://doi.org/10.2478/s13230-012-0013-4
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DOI: https://doi.org/10.2478/s13230-012-0013-4