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| Hierarchical Random Sampling Weak Random RRT Algorithm and Application for Motion Planning of Mobile Robot |
| ZHENG Wei1,ZHANG Tao1,WANG Hong-bin1,TIAN Ya-jing2,WANG Hong-rui3 |
1. Institute of Electric Engineering, Yanshan University, Qinhuangdao, Hebei 0660040, China
2. State Grid Gaocheng Power Supply Company, Shijiazhuang, Hebei 052160, China
3. Institute of Electronic and Information Engineering, Hebei University, Baoding, Hebei 071000, China |
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Abstract Aiming at the problems of rapid expansion random tree (RRT) has low sampling efficiency and large amount of node searching for nearest node, and nonlinear feedback controller is not subject to the dynamic constraints of the system model in mobile robots motion planning. A new weak RRT based on hierarchical random sampling and expansion is proposed, and a fast limiting amplitude nonlinear feedback controller is designed to ensure the robot can satisfy the dynamic constraints of the system model during the motion planning. Firstly, nodes to be expanded set are established at the beginning of the iteration in conjunction with the node evaluation strategy. Secondly, the nodes to be expanded are selected according to the prescribed order and the expansion direction is randomly selected, then the calculated new child node is connected to the random tree to complete the expansion. Thirdly, the initial path is planned by calculating the control sequence and posture of the path point of the mobile robot via the fast limiting amplitude nonlinear feedback controller to realize the motion planning of the mobile robot. Finally, the effectiveness of the proposed algorithm is verified through the simulations. The RRT based on hierarchical random sampling and expansion doesn’t depend on the selections of the nearest node, which reduces the solving solutions time of the RRT and increases the iteration speed.
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Received: 15 July 2020
Published: 24 September 2021
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2021, Vol. 42 
