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International Journal of Multimedia Computing, 2024, 5(1); doi: 10.38007/IJMC.2024.050101.

Improved A* Algorithm Based on Bessel Curve Optimization


Huiheng Suo, Tengsheng Yang, Qiang Hu, Jian Wu, Xie Ma, Qingwei Jia, Zizhen Chen, Xiushui Ma

Corresponding Author:
Huiheng Suo and Jian Wu

Nanchang Hangkong University, Nanchang, China


Aiming at the traditional A* algorithm in solving the robot path planning, there are the problems of longer path trajectory and computation time, more nodes for searching, and the path is not smooth enough. In this paper, an improved A* algorithm based on Bessel curve optimization is proposed. First, the traditional unidirectional search strategy of A* algorithm is changed to a bidirectional search strategy, which dynamically defines the target nodes of forward and reverse search; at the same time, an improved heuristic function is introduced to improve the search efficiency of A* algorithm by reducing the complexity of the planning space; and then the improved A* algorithm is combined with the Bessel curve optimization algorithm to eliminate the redundant inflection points in the robot's path, to make the path smoother and closer to the optimum. The experimental results show that the improved A* algorithm improves the efficiency of path planning, increases the stability and path smoothness, and is easier to apply in practice.


Improved A*, Bezier Curve, Path Planning, ROS Robots

Cite This Paper

Huiheng Suo, Tengsheng Yang, Qiang Hu, Jian Wu, Xie Ma, Qingwei Jia, Zizhen Chen, Xiushui Ma. Improved A* Algorithm Based on Bessel Curve Optimization. International Journal of Multimedia Computing (2024), Vol. 5, Issue 1: 1-13. https://doi.org/10.38007/IJMC.2024.050101.


[1] Liu Y, Huang RY, Xiong QH. Robot path planning based on improved A* algorithm. Automation and Instrumentation, 2015(4): 1-4.

[2] Jian Zhang, Liguo Liu, Wei Chen. Research on robot path planning based on Bessel curve. Mechanical Design and Manufacturing, 2017(7): 61-64.

[3] Y.Q. Wang, Robot path planning based on improved A* algorithm with Bessel curves. Nanjing: Nanjing University of Aeronautics and Astronautics, 2018.

[4] Xiao Yaming, Li Hongmei, Huang Jun, et al. A robot path planning method based on Bessel curves. Computer Applications and Software, 2015, 32(10): 188-191.

[5] Rong Cao, Research on robot path planning based on improved A* algorithm with Bessel curve. Automation and Instrumentation, 2016(1): 9-12.

[6] K. Karaman and E. Frazzoli. Sampling-Based Algorithms for Optimal Motion Planning. International Journal of Robotics Research, 30(7):846-894, 2011.

[7] Qi Li, Jian Zhang, Research on robot path planning algorithm based on Bessel curve. Robotics Technology and Application, 2017(1): 37-40.

[8]D. Fox, W. Burgard, and S. Thrun. The dynamic window approach to collision avoidance. IEEE Robotics & Automation Magazine, 4(1):23-33, 1997.

[9]state-of-the-art robot path planning techniques for unknown environments. In 2018 IEEE International Conference on Robotics and Biomimetics (ROBIO) (pp. 28-33).

[10]Mur-Artal, R., Montiel, J. M. M., & Tardós, J. D. (2015). ORB-SLAM: a versatile and accurate monocular SLAM system. IEEE Transactions on Robotics, 31(5), 1147-1163.

[11]Forster, C., Carlone, L., Dellaert, F., & Scaramuzza, D. (2017). On-manifold preintegration for real-time visual–inertial odometry. IEEE Transactions on Robotics, 33(1), 1-21.

[12] H. Zhang, SLAM-based mobile robot localisation and map construction. Beijing: Tsinghua University Press, 2011.

[13] Pan Jianwei, LiDAR technology and its application in UAV navigation and control. Computer Science and Applications, 2014, 4(2): 163-171.

[14] M. Gao, Robot path planning in unknown environment based on ant colony algorithm. Automation and Control, 2011(3): 23-26.

[15]Guo Jian, Zhang Hongxia, Li Li. Research on path planning algorithm in unknown environment based on UAV and mobile robot collaboration_Journal of Automation. 2015(3):550-558.