Path Planning Algorithms for Mobile Robots Based on Deep Reinforcement Learning

Authors

  • Zihan Zhang

DOI:

https://doi.org/10.54097/fa2r8310

Keywords:

Deep reinforcement learning, path planning algorithms, mobile robots.

Abstract

This paper gives an introduction of path planning algorithms for mobile robots based on deep reinforcement learning (DRL). Firstly, the traditional path planning algorithms are compared with the deep reinforcement learning path planning algorithms. One key advantage of DRL-based algorithms is their ability to meet real-time interactions due to lower calculation requirements. Then, some basic path planning algorithms based on deep learning used in unknown and dynamic environment are introduced. After that, the efforts on the improvement of the basic DRL-based path planning algorithms are discussed. Their training results are compared with the traditional path planning algorithms and the DRL-based path planning algorithms without improvements. Finally, the advantages and disadvantages of DRL-based path planning algorithms are analyzed. Although these algorithms have already a relative fast convergence speed and high success rate, they still do not meet the accuracy requirement for sophisticated work. Besides, the train period is also expected to be shorten further. The potential improvements in the future are also pointed.

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References

[1] Karur K, Sharma N, Dharmatti C, Siegel JE. A Survey of Path Planning Algorithms for Mobile Robots. Vehicles, 2021, 3 (3): 448-468.

[2] Qin H, Shao S, Wang T, Yu X, Jiang Y, Cao Z. Review of Autonomous Path Planning Algorithms for Mobile Robots. Drones, 2023, 7 (3): 211.

[3] Han H, Wang J, Kuang L, Han X, Xue H. Improved Robot Path Planning Method Based on Deep Reinforcement Learning. Sensors, 2023, 23 (12): 5622.

[4] Qin H, Qiao B, Wu W, Deng Y. A Path Planning Algorithm Based on Deep Reinforcement Learning for Mobile Robots in Unknown Environment. In Proceedings of the 2022 IEEE 5th Advanced Information Management, Communicates, Electronic and Automation Control Conference (IMCEC), Chongqing, China, 2022: 1661-1666.

[5] Cheng Z, Li B, Liu B. Research on Path Planning of Mobile Robot Based on Dynamic Environment. In Proceedings of the 2022 IEEE International Conference on Mechatronics and Automation (ICMA), Guilin, Guangxi, China, 2022: 140-145.

[6] Hu Y, Li D, He Y, Han J. Incremental Learning Framework for Autonomous Robots Based on Q-Learning and the Adaptive Kernel Linear Model. IEEE Transactions on Cognitive and Developmental Systems, 2022, 14 (1): 64-74.

[7] Gao J, et al. Deep reinforcement learning for indoor mobile robot path planning. Sensors, 2020, 20 (19): 5493.

[8] Zhang J, Zhao H. Mobile Robot Path Planning Based on Improved Deep Reinforcement Learning Algorithm. In Proceedings of the 2024 4th International Conference on Neural Networks, Information and Communication Engineering (NNICE), Guangzhou, China, 2024: 1758-1761.

[9] Yang Y, Zhang L, Guo H. The Path Planning Research for Mobile Robot Based on Reinforcement Learning Particle Swarm Algorithm. In Proceedings of the 2024 7th International Conference on Advanced Algorithms and Control Engineering (ICAACE), Shanghai, China, 2024: 1577-1580.

[10] Ruqing Z, Xin L, Shubin L, Jihuai Z, Fusheng L. Deep Reinforcement Learning Based Path Planning for Mobile Robots Using Time-Sensitive Reward. In Proceedings of the 2022 19th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP), Chengdu, China, 2022: 1-4.

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Published

31-10-2024

How to Cite

Zhang, Z. (2024). Path Planning Algorithms for Mobile Robots Based on Deep Reinforcement Learning. Highlights in Science, Engineering and Technology, 114, 49-55. https://doi.org/10.54097/fa2r8310