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A Dynamic Parameter Adaptive Path Planning Algorithm

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Combinatorial Optimization and Applications (COCOA 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14462))

Abstract

Path planning in complex environments has always been a focus of research for scholars both domestically and internationally. This study addresses the challenge of path planning that combines obstacle avoidance and optimal path searching in scenarios lacking prior knowledge. The proposed approach introduces a parameter dynamic adaptation strategy for path planning. Experimental investigations are conducted using grid-based maps, and the results demonstrate that the method presented in this paper surpasses Q-learning and Sarsa algorithms in terms of comprehensive exploration, enhanced stability, and quicker convergence speed.

This research is supported by National Natural Science Foundation of China under Grant Nos. 62272359 and 62172322; Natural Science Basic Research Program of Shaanxi Province under Grant Nos. 2023JC-XJ-13 and 2022JM-367.

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References

  1. Chen, H., Ji, Y., Niu, L.: Reinforcement learning path planning algorithm based on obstacle area expansion strategy. Intell. Serv. Robot. 13(2), 289–297 (2020)

    Article  Google Scholar 

  2. Devo, A., Costante, G., Valigi, P.: Deep reinforcement learning for instruction following visual navigation in 3D maze-like environments. IEEE Rob. Autom. Lett. 5(2), 1175–1182 (2020)

    Article  Google Scholar 

  3. Dijkstra, E.W.: A note on two problems in connexion with graphs. In: Edsger Wybe Dijkstra: His Life, Work, and Legacy, pp. 287–290 (2022)

    Google Scholar 

  4. Hart, P.E., Nilsson, N.J., Raphael, B.: A formal basis for the heuristic determination of minimum cost paths. IEEE Trans. Syst. Sci. Cybern. 4(2), 100–107 (1968)

    Article  Google Scholar 

  5. Jiang, L., Huang, H., Ding, Z.: Path planning for intelligent robots based on deep q-learning with experience replay and heuristic knowledge. IEEE/CAA J. Automatica Sinica 7(4), 1179–1189 (2019)

    Article  MathSciNet  Google Scholar 

  6. Patle, B., Pandey, A., Parhi, D., Jagadeesh, A., et al.: A review: on path planning strategies for navigation of mobile robot. Defence Technol. 15(4), 582–606 (2019)

    Article  Google Scholar 

  7. Polydoros, A.S., Nalpantidis, L.: Survey of model-based reinforcement learning: applications on robotics. J. Intell. Rob. Syst. 86(2), 153–173 (2017)

    Article  Google Scholar 

  8. Santiago, R.M.C., De Ocampo, A.L., Ubando, A.T., Bandala, A.A., Dadios, E.P.: Path planning for mobile robots using genetic algorithm and probabilistic roadmap. In: 2017IEEE 9th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management (HNICEM), pp. 1–5. IEEE (2017)

    Google Scholar 

  9. Sutton, R.S., Barto, A.G.: Reinforcement Learning: An Introduction. MIT press, Cambridge (2018)

    Google Scholar 

  10. Szepesvári, C.: Algorithms for Reinforcement Learning. Springer, Heidelberg (2022). https://doi.org/10.1007/978-3-031-01551-9

    Book  Google Scholar 

  11. Wei, J., De Hua, Z., Shuangbao, M., Gaocheng, Y., Wei, C.: Dynamic walking characteristics and control of four-wheel mobile robot on ultra-high voltage multi-split transmission line. Trans. Inst. Meas. Control. 44(6), 1309–1322 (2022)

    Article  Google Scholar 

  12. Yang, Y., Juntao, L., Lingling, P.: Multi-robot path planning based on a deep reinforcement learning DQN algorithm. CAAI Trans. Intell. Technol. 5(3), 177–183 (2020)

    Article  Google Scholar 

  13. Zhang, H.Y., Lin, W.M., Chen, A.X.: Path planning for the mobile robot: a review. Symmetry 10(10), 450 (2018)

    Article  Google Scholar 

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Authors and Affiliations

  1. Institute of Computing Theory and Technology, and ISN Laboratory, Xidian University, Xi’an, 710071, China

    Guangyu Yao, Nan Zhang, Zhenhua Duan & Cong Tian

Authors
  1. Guangyu Yao
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  2. Nan Zhang
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  3. Zhenhua Duan
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  4. Cong Tian
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Corresponding author

Correspondence to Nan Zhang .

Editor information

Editors and Affiliations

  1. University of Texas at Dallas, Richardson, TX, USA

    Weili Wu

  2. Beijing Normal University, Zhuhai, China

    Jianxiong Guo

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Yao, G., Zhang, N., Duan, Z., Tian, C. (2024). A Dynamic Parameter Adaptive Path Planning Algorithm. In: Wu, W., Guo, J. (eds) Combinatorial Optimization and Applications. COCOA 2023. Lecture Notes in Computer Science, vol 14462. Springer, Cham. https://doi.org/10.1007/978-3-031-49614-1_17

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  • DOI: https://doi.org/10.1007/978-3-031-49614-1_17

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-49613-4

  • Online ISBN: 978-3-031-49614-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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