Mobile Robot Path Planning Based on Improved A* Algorithm and Dynamic Window Method

被引：0

作者：

Wang H. ^{[1
]}

Yin P. ^{[1
]}

Zheng W. ^{[1
]}

Wang H. ^{[1
]}

Zuo J. ^{[1
]}

机构：

[1] Key Lab of Industrial Computer Control Engineering of Hebei Province, Yanshan University, Qinhuangdao

来源：

Jiqiren/Robot | 2020年 / 42卷 / 03期

关键词：

Dynamic window method; Improved A* algorithm; Mobile robot; Path planning;

D O I：

10.13973/j.cnki.robot.190305

中图分类号：

学科分类号：

摘要：

A hybrid algorithm combining the improved A* algorithm and the dynamic window method is proposed, to solve the problem of mobile robot path planning in multi-target complex environments. In order to improve the algorithm efficiency by planning a path passing through multiple target points in a single run, and to improve the flexibility of path smoothing and meet the non-holonomic constraints of mobile robots, the target cost function is used to prioritize all targets firstly. Furthermore, the improved A* algorithm is used to plan an optimal path passing through multiple target points. Meanwhile, the adaptive arc optimization algorithm and the weighted obstacle step adjustment algorithm are used to effectively shorten the path length by 5% and reduce the total turning angle by 26.62%. Secondly, an online path planning method combining the improved dynamic window algorithm and the global path planning information is proposed for mobile robots to avoid the local obstacles and pursue the dynamic target points in dynamic complex environments. The preview-deviation-yaw based tracking method is used to successfully capture the moving target points and improve the path planning efficiency. Finally, the simulation experiments with the proposed method are carried out, and the results show that it can achieve the path planning more effectively in complex dynamic environments. © 2020, Science Press. All right reserved.

引用

页码：346 / 353

页数：7

共 17 条

[1]

Sariff N., Buniyamin N., An overview of autonomous mobile robot path planning algorithms, 4th Student Conference on Research and Development, pp. 182-187, (2007)

[2]

Mac T.T., Copot C., Tran D.T., Et al., A hierarchical global path planning approach for mobile robots based on multi-objective particle swarm optimization, Applied Soft Computing, 59, pp. 68-76, (2017)

[3]

Song B.Y., Wang Z.D., Zou L., On global smooth path planning for mobile robots using a novel multimodal delayed PSO algorithm, Cognitive Computation, 9, 1, pp. 5-17, (2017)

[4]

Chen H., Fei J.Y., UAV path planning based on particle swarm optimization with global best path competition, International Journal of Pattern Recognition and Artificial Intelligence, 32, 6, (2018)

[5]

Guo J.M., Liu L., Liu Q., Et al., An improvement of D* algorithm for mobile robot path planning in partial unknown environment, 2nd International Conference on Intelligent Computation Technology and Automation, pp. 394-397, (2009)

[6]

Nieuwenhuisen M., Behnke S., Layered mission and path planning for MAV navigation with partial environment knowledge, Advances in Intelligent Systems and Computing, 302, pp. 307-319, (2016)

[7]

Zhao X., Wang Z., Huang C.K., Et al., Path planning of mobile robot based on improved A* algorithm, Robot, 40, 6, pp. 903-910, (2018)

[8]

Wang D.J., Path planning of indoor mobile robot based on improved A* algorithm, Journal of Tsinghua University (Science and Technology), 52, 8, pp. 1085-1089, (2012)

[9]

Yuan Q., Han C.S., Research on robot path planning based on smooth A* algorithm for different grid scale obstacle environment, Journal of Computational and Theoretical Nanoscience, 13, 8, pp. 5312-5321, (2016)

[10]

Osmankovic D., Tahirovic A., Magnani G., All terrain vehicle path planning based on D* lite and MPC based planning paradigm in discrete space, IEEE International Conference on Advanced Intelligent Mechatronics, pp. 334-339, (2017)

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