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Enhanced A* Algorithm for the Time Efficient Navigation of Unmanned Vehicle by Reducing the Uncertainty in Path Length Optimization

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Abstract

Optimal path planning is considered as a crucial problem in mobile robotics. The conventional A* algorithm is primarily focused on the heuristic values of nodes. However, during the experimental analysis, it observed that the occupied node due to obstacles, effect the selection of shortest and time efficient path in conventional A*. Therefore, a modified bidirectional path planning technique—Time Optimized A* (TOA*) algorithm is proposed that always choose the time efficient and shortest path with lesser number of operations to reach the destination from start position. The proposed TOA* algorithm is trial in various simulated tests and the execution time is reduce by 33.75% (Conventional A*), 71.47% (Breadth First Search) and 67.66% (Jump point search) is observed likewise, a reduction in number of operations by 66.14% (conventional A*), 91.35% (Breadth First Search) and 89.80% (Jump point search) is observed. The proposed TOA* is also tested on a mobile robot in real-world experiments and a significant reduction of 31.33% in number of closed nodes and 14.08% reduction in sharp turns has achieved. The reduction of sharp turns with lesser number of operations resulted in reduction in total time by 5.63% and the acceleration of mobile robot is increased by 7.01% proportionate to conventional A*.

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References

  1. R. Singh, Optimized trajectory planning for the time efficient navigation of mobile robot in constrained environment, Int. J. Mach. Learn. Cyber, (2022) 1–25. https://doi.org/10.1007/s13042-022-01684-7.

  2. R. Siegwart, I.R. Nourbakhsh and D. Scaramuzza, Introduction to Autonomous Mobile Robots; MIT Press (2011).

  3. E. Galceran and M. Carreras, A survey on coverage path planning for robotics, Robotics and Autonomous Systems, 61(12) (2013) 1258–1276. https://doi.org/10.1016/j.robot.2013.09.004.

    Article  Google Scholar 

  4. R. Singh and K.S. Nagla, Comparative analysis of range sensors for the robust autonomous navigation—a review. Sensor Review, 40 (2019) 17–41. https://doi.org/10.1108/SR-01-2019-0029.

    Article  Google Scholar 

  5. S. Kumar and A. Sikander, An intelligent optimize path planner for efficient mobile robot path planning in a complex terrain. Microsystem Technologies, (2022) 1–19. https://doi.org/10.1007/s00542-022-05322-8.

  6. S. Kumar and A. Sikander, A modified probabilistic roadmap algorithm for efficient mobile robot path planning. Engineering Optimization, (2022) 1–19. https://doi.org/10.1080/0305215X.2022.2104840.

  7. J.A. Oroko and G.N. Nyakoe, Obstacle avoidance and path planning schemes for autonomous navigation of a mobile robot: a review. In Proceedings of the Sustainable Research and Innovation Conference, (2022) pp. 314–318.

  8. M. Hawa, Light-assisted A* path planning, Engineering Applications of Artificial Intelligence, 26(2) (2013) 888–898. https://doi.org/10.1016/j.engappai.2012.08.010.

    Article  Google Scholar 

  9. B. Siciliano, O. Khatib, eds., Springer Handbook of Robotics; Springer (2016) pp. 1577–1604. https://doi.org/10.1007/978-3-319-32552-1.

  10. P. Urcola, M.T. Lázaro, J.A. Castellanos and L. Montano, Cooperative minimum expected length planning for robot formations in stochastic maps, Robotics and Autonomous Systems, 87 (2017) 38–50. https://doi.org/10.1016/j.robot.2016.09.002.

    Article  Google Scholar 

  11. R. Singh and K.S. Nagla, Improved 2D laser grid mapping by solving mirror reflection uncertainty in SLAM, International Journal of Intelligent Unmanned Systems, 6 (2018) 93–114. https://doi.org/10.1108/IJIUS-01-2018-0003.

    Article  Google Scholar 

  12. P.E. Hart, N.J. Nilsson and B. Raphael, A formal basis for the heuristic determination of minimum cost paths, IEEE transactions on Systems Science and Cybernetics, 4 (1968) 100–107. https://doi.org/10.1109/TSSC.1968.300136.

    Article  Google Scholar 

  13. F. Duchoň, D. Huňady, M. Dekan and A. Babinec, Optimal navigation for mobile robot in known environment. In Applied Mechanics and Materials; Trans Tech Publications Ltd., (2013), vol. 282, pp. 33–38. https://doi.org/10.4028/www.scientific.net/AMM.282.33.

  14. F. DuchoĖ, A. Babineca, M. Kajana, P. BeĖoa, M. Floreka, T. Ficoa and L. Jurišicaa, Path planning with modified a star algorithm for a mobile robot, Procedia Engineering, 96 (2014) 59–69. https://doi.org/10.1016/j.proeng.2014.12.098.

    Article  Google Scholar 

  15. B. Fu, L. Chen, Y. Zhou, D. Zheng, Z. Wei, J. Dai and H. Pan, An improved A* algorithm for the industrial robot path planning with high success rate and short length, Robotics and Autonomous Systems, 106 (2018) 26–37. https://doi.org/10.1016/j.robot.2018.04.007.

    Article  Google Scholar 

  16. B. Li, H. Liu and W. Su, Topology optimization techniques for mobile robot path planning, Applied Soft Computing, 78 (2019) 528–544. https://doi.org/10.1016/j.asoc.2019.02.044.

    Article  Google Scholar 

  17. Y. Deng, Y. Chen, Y. Zhang and S. Mahadevan, Fuzzy Dijkstra algorithm for shortest path problem under uncertain environment, Applied Soft Computing, 12(3) (2012) 1231–1237. https://doi.org/10.1016/j.asoc.2011.11.011.

    Article  Google Scholar 

  18. T.T. Mac, C. Copot, D.T. Tran and R. De Keyser, Heuristic approaches in robot path planning: A survey, Robotics and Autonomous Systems, 86 (2016) 13–28. https://doi.org/10.1016/j.robot.2016.08.001.

    Article  Google Scholar 

  19. M.S. Masmoudi, N. Krichen, M. Masmoudi and N. Derbel, Fuzzy logic controllers design for omnidirectional mobile robot navigation, Applied Soft Computing, 49 (2016) 901–919. https://doi.org/10.1016/j.asoc.2016.08.057.

    Article  Google Scholar 

  20. M. Davoodi, Bi-objective path planning using deterministic algorithms, Robotics and Autonomous Systems, 93 (2017) 105–115. https://doi.org/10.1016/j.robot.2017.03.021.

    Article  Google Scholar 

  21. H.E. Tseng, C.C. Chang, S.C. Lee and Y.M. Huang, Hybrid bidirectional ant colony optimization (hybrid BACO): An algorithm for disassembly sequence planning, Engineering Applications of Artificial Intelligence, 83 (2019) 45–56. https://doi.org/10.1016/j.engappai.2019.04.015.

    Article  Google Scholar 

  22. D. Harabor and A. Grastien, The JPS pathfinding system. In International Symposium on Combinatorial Search; (2012), vol. 3, no. 1.

  23. N. Lipovetzky, H. Geffner, Best-first width search: Exploration and exploitation in classical planning. In 31st AAAI Conference on Artificial Intelligence; (2017). https://doi.org/10.1609/aaai.v31i1.11027

  24. S. Long, D. Gong, X. Dai and Z. Zhang, Mobile robot path planning based on ant colony algorithm with A* heuristic method, Frontiers in Neurorobotics, 13 (2019) 15. https://doi.org/10.3389/fnbot.2019.00015.

    Article  Google Scholar 

  25. D. Green, Procedural Content Generation for C++ Game Development; Packt Publishing Ltd, (2016)

  26. Z. Xie and Z.W. Zhong, Aircraft path planning under adverse weather conditions. In MATEC Web of Conferences; EDP Sciences, (2016), vol. 77, p. 15001. https://doi.org/10.1051/matecconf/20167715001.

  27. M.A. Contreras-Cruz, V. Ayala-Ramirez and U.H. Hernandez-Belmonte, Mobile robot path planning using artificial bee colony and evolutionary programming, Applied Soft Computing, 30 (2015) 319–328. https://doi.org/10.1016/j.asoc.2015.01.067.

    Article  Google Scholar 

  28. M.A. Juman, Y.W. Wong, R.K. Rajkumar, K.W. Kow and Z.W. Yap, An incremental unsupervised learning based trajectory controller for a 4 wheeled skid steer mobile robot, Engineering Applications of Artificial Intelligence, 85 (2019) 385–392. https://doi.org/10.1016/j.engappai.2019.06.023.

    Article  Google Scholar 

  29. S. Pérez-Carabaza, J. Scherer, B. Rinner, J.A. López-Orozco and E. Besada-Portas, UAV trajectory optimization for Minimum Time Search with communication constraints and collision avoidance, Engineering Applications of Artificial Intelligence, 85 (2019) 357–371. https://doi.org/10.1016/j.engappai.2019.06.002.

    Article  Google Scholar 

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  1. Instrumentation and Control Engineering Department, Dr B.R. Ambedkar National Institute of Technology, Jalandhar, India

    Raj Kumar Singh & K. S. Nagla

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Singh, R.K., Nagla, K.S. Enhanced A* Algorithm for the Time Efficient Navigation of Unmanned Vehicle by Reducing the Uncertainty in Path Length Optimization. MAPAN 38, 317–335 (2023). https://doi.org/10.1007/s12647-022-00618-6

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