Chongyang Lv, Xuejie Fan, and Mingxiao Sun


  1. [1] L. Liu, J. Yao, D. He, J. Chen, J. Huang, H. Xu, B. Wang,and J. Guo, Global dynamic path planning fusion algorithmcombining jump-A algorithm and dynamic window approach,IEEE Access, 9, 2021, 19632–19638.
  2. [2] D. Zhu, C. Tian, B. Sun, and C. Luo, Complete coverage pathplanning of autonomous underwater vehicle based on GBNNalgorithm, Journal of Intelligent and Robotic Systems, 94(1),2019, 237–249.
  3. [3] L. Zhang, Y. Zhang, and Y. Li, Mobile robot path planningbased on improved localized particle swarm optimization, IEEESensors Journal, 21(5), 2021, 6962–6972.
  4. [4] Y. Zhang and S. Wang, LSPP: A novel path planning algorithmbased on perceiving line segment feature, IEEE SensorsJournal, 22(1), 2022, 720–731.
  5. [5] L. Lacasa, B. Luque, F. Ballesteros, J. Luque, and J.C. Nuo,From time series to complex networks: The visibility graph,Proceedings of the National Academy of Sciences, 105(13),2008, 4972–4975.
  6. [6] D. Wang, Indoor mobile robot path planning based on improvedA algorithm, Journal of Tsinghua University (Science andTechnology), 52, 2012, 1085–1089.
  7. [7] Q. Jin, C. Tang, and W. Cai, Research on dynamic pathplanning based on the fusion algorithm of improved ant colonyoptimization and rolling window method, IEEE Access, 10,2021, 28322–28332.
  8. [8] Z. Wang, G. Li, and J. Ren, Dynamic path planning forunmanned surface vehicle in complex offshore areas based104on hybrid algorithm, Computer Communications, 166, 2021,49–56.
  9. [9] W. Shan and Z. Meng, Design of smooth path based on improvedA algorithm, Journal of Southeast University (Natural ScienceEdition), 40(S1), 2010, 155–161.
  10. [10] Z. Qin, X. Chen, M. Hu, L. Chen, and J. Fan, A novel pathplanning methodology for automated valet parking based ondirectional graph search and geometry curve, Robotics andAutonomous Systems, 132, 2020, 103606.
  11. [11] C. Lin, H. Wang, J. Yuan, and M. Fu, An online path planningmethod based on hybrid quantum ant colony optimizationfor AUV, International Journal of Robotics and Automation,33(4), 2018, 435–444.
  12. [12] B. Fu, L. Chen, Y. Zhou, D. Zheng, Z. Wei, J. Dai, and H. Pan,“An improved A algorithm for the industrial robot pathplanning with high success rate and short length,” Roboticsand Autonomous Systems, 106, 2018, 26–37.
  13. [13] X. Zhong, J. Tian, H. Hu, and X. Peng, Hybrid path planningbased on safe A algorithm and adaptive window approach formobile robot in large-scale dynamic environment, Journal ofIntelligent and Robotic Systems, 99(1), 2020, 65–77.
  14. [14] H. Wang, S. Lou, J. Jing, Y. Wang, W. Liu, and T. Liu,The EBS-A algorithm: An improved A algorithm for pathplanning, PLoS One, 17(2), 2022, e0263841.
  15. [15] Z. Chen, Y. Zhang, Y. Zhang, Y. Nie, J. Tang, and S. Zhu,A hybrid path planning algorithm for unmanned surfacevehicles in complex environment with dynamic obstacles, IEEEAccess, 7, 2019, 126439–126449.
  16. [16] H. Wang, C. Hao, P. Zhang, M. Zhang, P. Yin, and Y. Zhang,Path planning of mobile robots based on A algorithm andartificial potential field method, China Mechanical Engineering,30(20), 2019, 2489–2496.
  17. [17] F. Duchoˇn, A. Babinec, M. Kajan, P. Beˇno, M. Florek, T. Fico,and L. Juriˇsica, Path planning with modified a star algorithmfor a mobile robot, Procedia Engineering, 96, 2014, 59–69.
  18. [18] X. Ji, S. Feng, Q. Han, H. Yin, and S. Yu, Improvementand fusion of A algorithm and dynamic window approachconsidering complex environmental information, ArabianJournal for Science and Engineering, 46(8), 2021, 7445–7459.
  19. [19] Z. Liu, H. Liu, Z. Lu, and Q. Zeng, A dynamic fusion pathfindingalgorithm using delaunay triangulation and improved a-starfor mobile robots, IEEE Access, 9, 2021, 20602–20621.
  20. [20] Y. Guo, Q. Liu, J. Bao, F. Xu, and W. Lv, Review of research onAUV obstacle avoidance algorithms based on artificial potentialfield method, Computer Engineering and Applications, 56(4),2020, 8.
  21. [21] J. Yu, W. Deng. Z. Zhao, X. Wang, J. Xu, L. Wang, Q. Sun,and Z. Shen, A hybrid path planning method for an unmannedcruise ship in water quality sampling, IEEE Access, 7, 2019,87127–87140.
  22. [22] X. Wang, L. Yang, Y. Zhang, and S. Meng, Robot path planningbased on improved ant colony algorithm with potential fieldheuristic, Control and Decision, 33(10), 2018, 7.
  23. [23] S. Zhu, W. Zhu, X. Zhang, and T. Cao, Path planning of lunarrobot based on dynamic adaptive ant colony algorithm andobstacle avoidance, International Journal of Advanced RoboticSystems, 17(3), 2020, 4149–4171.
  24. [24] J. Li, B. Xu, Y. Yang, and H. Wu, Three-phase qubits-basedquantum ant colony optimization algorithm for path planningof automated guided vehicles, International Journal of Roboticsand Automation, 34(2), 2019, 156–163.
  25. [25] D. Zhu, C. Cheng, and B. Sun, An integrated AUV pathplanning algorithm with ocean current and dynamic obstacles,International Journal of Robotics and Automation, 31(5), 2016,382–389.
  26. [26] P. Das, H. Behera, S. Das, H. Tripathy, B. Panigrahi, andS. Pradhan, A hybrid improved PSO-DV algorithm for multi-robot path planning in a clutter environment, Neurocomputing,207, 2016, 735–753.
  27. [27] B. Kov´acs, G. Szayer, F. Tajti, M. Burdelis, and P. Korondi,A novel potential field method for path planning of mobilerobots by adapting animal motion attributes, Robotics andAutonomous Systems, 82, 2016, 24–34.

Important Links:

Go Back