Open Access
Issue
ITM Web Conf.
Volume 47, 2022
2022 2nd International Conference on Computer, Communication, Control, Automation and Robotics (CCCAR2022)
Article Number 02030
Number of page(s) 6
Section Algorithm Optimization and Application
DOI https://doi.org/10.1051/itmconf/20224702030
Published online 23 June 2022
  1. Xiang Rong T, Yu kun Z, Xin Xin J. Improved A-star algorithm for robot path planning in static environment[C]//Journal of Physics: Conference Series. IOP Publishing, 2021, 1792(1): 012067 [CrossRef] [Google Scholar]
  2. Zhu D D, Sun J Q. A new algorithm based on dijkstra for vehicle path planning considering intersection attribute [J]. IEEE Access, 2021, 9: 19761–19775. [CrossRef] [Google Scholar]
  3. BLASI L, D’AMATO E, MATTEI M, et al. Path planning and real-time collision avoidance based on the essential visibility graph[J]. Applied Sciences, 2020, 10 (16): 5613. [CrossRef] [Google Scholar]
  4. Zhang Xu, Zeng Xiangxin, Lang Bo. Path planning of free-floating space robot based on control variable parameterization method [J]. Optical precision engineering, 2019, 27 (02): 372–378. [CrossRef] [Google Scholar]
  5. LEE H Y, SHIN H, CHAE J. Path Planning for mobile agents using a genetic algorithm with a direction guided factor [J]. Electronics, 2018, 7 (10): 212–232. [CrossRef] [Google Scholar]
  6. Fox D, Burgard W, Thrun S. Controlling synchro-drive robots with the dynamic window approach to collision avoidance[C]//Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems. IROS’96. IEEE, 1996, 3: 1280–1287. [Google Scholar]
  7. Qu Yuanrang, Xue Jianru, Zhu Yaoguo, Xiao Peng. VFH algorithm for unmanned vehicle motion planning problem [J]. Computer simulation, 2018, 35 (12): 245–251. [Google Scholar]
  8. Huang Bingqiang, Cao Guangyi. Research on mobile robot path planning based on artificial potential field method [J]. Computer engineering and application, 2006, 42 (27): 26–28. [Google Scholar]
  9. LIU Q, SHI L, SUN L, et al. Path planning for UAV-mounted mobile edge computing with deep reinforcement learning [J]. IEEE Transactions on Vehicular Technology, 2020, 69 (5): 5723–5728. [CrossRef] [Google Scholar]
  10. Zheng Bolong, Ming Lingfeng, Hu Qi, Fang Yixiang, Zheng Kai, and Li Guohui. Dynamic path planning of online Car-hailing based on deep reinforcement learning [J]. Computer research and development, 2022, 59 (02): 329–341. [Google Scholar]
  11. Hu Xiaohui. A reinforcement learning action selection mechanism based on dynamic parameter adjustment [J]. Computer engineering and application, 2008, 44 (28): 29–31. [Google Scholar]

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