Path planning for mobile robot based on improved genetic algorithm

被引：0

作者：

Wei T. ^{[1
]}

Long C. ^{[1
]}

机构：

[1] School of Instrumentation and Optoelectronic Engineering, Beihang University, Beijing

来源：

Beijing Hangkong Hangtian Daxue Xuebao/Journal of Beijing University of Aeronautics and Astronautics | 2020年 / 46卷 / 04期

关键词：

Genetic algorithm; Inter-frame correlation; Mobile robot; Obstacle avoidance; Path planning;

D O I：

10.13700/j.bh.1001-5965.2019.0298

中图分类号：

学科分类号：

摘要：

Path planning is the key technology to realize autonomous navigation of mobile robots. For the problem that the path length is not the shortest and the path is not coherent in the two plan cycles with conventional path planning method, a new method for inter-frame correlation smooth path planning based on improved genetic algorithm is proposed. Firstly, the candidate paths were generated by combining random and directional search methods. Then, the insertion operator and deletion operator were added to conventional genetic operators, and the path coherence of two plan cycles was considered in the fitness function to calculate the fitness value of each candidate path. Finally, the path with the highest fitness value was output as the current optimal path. Simulation results show that the proposed method is correct and feasible. Experimental results show that, compared with A* algorithm and conventional genetic algorithm, the path length of mobile robot is reduced by 3.05% and 1.85%, the variation of maximum yaw angle is reduced by 38.02% and 32.43%, and the sum of absolute value of turning angle is reduced by 23.97% and 19.94% respectively during the movement of mobile robot. It shows that the resulting path of this method is more optimal, which observably improves the moving efficiency and stationarity of the mobile robot. © 2020, Editorial Board of JBUAA. All right reserved.

引用

页码：703 / 711

页数：8

共 18 条

[1]

Mac T.T., Copot C., Tran D.T., Et al., Heuristic approaches in robot path planning: A survey, Robotics and Autonomous Systems, 86, pp. 13-28, (2016)

[2]

Zhao X., Wang Z., Huang C.K., Et al., Mobile robot path planning based on improved A* algorithm, Robot, 40, 6, pp. 137-144, (2018)

[3]

Zhou H.Z., Hu X.M., Cehn L., Et al., Dynamic path planning obstacle avoidance algorithm for autonomous driving, Computer Application, 37, 3, pp. 883-888, (2017)

[4]

Paden B., Cap M., Yong S.Z., Et al., A survey of motion planning and control techniques for self-driving urban vehicles, IEEE Transactions on Intelligent Vehicles, 1, 1, pp. 33-55, (2016)

[5]

Alia C., Gilles T., Reine T., Et al., Local trajectory planning and tracking of autonomous vehicles, using clothoid tentacles method, IEEE Symposium on Intelligent Vehicles, pp. 674-679, (2015)

[6]

Chu K., Lee M., Sunwoo M., Local path planning for off-road autonomous driving with avoidance of static obstacles, IEEE Transactions on Intelligent Transportation Systems, 13, 4, pp. 1599-1616, (2012)

[7]

Wang J.C., Zhang X.T., Chen B., Et al., Heuristic optimization path-finding algorithm based on Dijkstra algorithm, Journal of University of Science and Technology Beijing, 29, 3, pp. 346-350, (2007)

[8]

Wang Q., Huang W.H., Liu B., Et al., An improved A* algorithm for path-planning of two-wheeled self-balancing vehicle, IEEE Conference on Industrial Electronics and Applications(ICIEA), pp. 841-846, (2018)

[9]

Xin Y., Liang H.W., Du M.B., Et al., An Improved A* Algorithm for Searching Infinite neighborhood, Robot, 36, 5, pp. 627-633, (2014)

[10]

Xu W.D., Wei J.Q., Dolan J.M., Et al., A real-time motion planner with trajectory optimization for autonomous vehicles, IEEE International Conference on Robotics and Automation, pp. 2061-2067, (2012)

← 1 2 →