|
|
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 |
|
|
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.
|
Received: 15 July 2020
Published: 24 September 2021
|
|
|
|
|
[1]赵春芳, 李江昊, 张大伟. 基于改进免疫遗传优化蚁群算法的移动机器人路径寻优研究 [J]. 计量学报, 2019, 40 (3): 155-160.
Zhao C F, Li J H, Zhang D W. Robot Path Optimization Research Based on Improved Immune Genetic Optimiza-tion Ant Colony Algorithm [J]. Acta Metrologica Sinica, 2019, 40 (3): 155-160.
[2]朱奇光, 王梓巍, 陈颖. 基于图像匹配的移动机器人导航研究 [J]. 计量学报, 2017, 38 (5): 571-575.
Zhu Q G, Wang Z W, Chen Y. A Research for Mobile Robot Navigation Based on Image Matching [J]. Acta Metrologica Sinica,2017, 38 (5): 571-575.
[3]Lavalle S M. Rapidly-exploring random trees: a new tool for path palnning [J]. Computerence Dept Oct,1998,16(5):367-375.
[4]阮晓钢, 周静, 张晶晶, 等. 基于子目标搜索的机器人目标导向RRT路径规划算法 [J]. 控制与决策, 2020, 35 (10): 2543-2548.
Ruan X G, Zhou J, Zhang J J, et al. Robot goal guide RRT path planning base on subtarget search [J]. Control and Decision. 2020, 35 (10): 2543-2548.
[5]Allen R, Pavone M. A real-time framework for kinodyn-amic planning with application to quadrotor obstacle avoidance [C]//AIAA Guidance Navigation and Control Conference.San Francisco, USA,2016: 1374-1392.
[6]Li Y, Cui R X, Li Z J, et al. Neural network approxima-tion based near-optimal motion planning with kinodynamic constraints using RRT [J]. IEEE Transactions on Industrial Electronics, 2018, 11 (65): 8718-8729.
[7]袁静妮, 杨林, 唐晓峰, 等.基于改进RRT*与行驶轨迹优化的智能汽车运动规划[J/OL]. 自动化学报, 2020: 1-10. https://doi.org/10.16383/j.aas.c190607.
[8]Chen Y, He Z. Horizon-based lazy optimal RRT for fast, efficient replanning in dynamic environment [J]. Autonomous Robots, 2019, 43 (8): 2271-2292.
[9]李洋, 徐达. 基于引力自适应步长RRT的双臂机器人协同路径规划 [J]. 机器人, 2020, 42 (5): 606-616.
Li Y, Xu D. Cooperative Path Planning of Dual-arm Base on Attractive Force Self-adaptive Step Size RRT [J]. Robot, 2020, 42 (5): 606-616.
[10]Xu J, Song K. A batch informed samplingbased algori-thm for fast anytime asymptotically-optimal motion plan-ning in cluttered environments [J]. Expert Systems with Applications, 2020, 144: 113124.
[11]Mashayekhi R, Idris M Y I. Informed RRT*-connect: an asymptotically optimal single-query path planning me-thod [J]. IEEE Access, 2020, 8 (1): 19842-19852.
[12]Mashayekhi R, Idris M Y I. Hybrid RRT: a semi-dual-tree RRT-based motion planner [J]. IEEE Access, 2020, 8 (1): 18658-18668.
[13]Wang X Y, Li X J, Guan Y. Bidirectional potential guided RRT* for motion planning [J]. IEEE Access, 2019, 7 (1): 95046-95057.
[14]Zhang H, Wang Y. Path planning of industrial robot based on improved RRT algorithm in complex environ-ments [J]. IEEE Access, 2018, 6 (1): 53296-53306.
[15]Liu B, Feng W. A variable-step RRT* path planning algorithm for quadrotors in below-canopy [J]. IEEE Access, 2020, 8 (1): 62980-62989.
[16]Li Y, Cui R. Neural network approximation based near-optimal motion planning with kinodynamic constraints using RRT [J]. IEEE Transactions on Industrial Electronics, 2018, 65 (11): 8718-8729.
[17]Li. Y , Wei W. PQ-RRT*: an improved path planning algorithm for mobile robots [J]. Expert Systems with Applications, 2020, 152: 113425.
[18]Jeong I B, Lee S J. Quick-RRT*: triangular inequa-litybased implementation of RRT* with improved initial solution and convergence rate [J]. Expert Systems with Applications, 2019, 123 (1): 82-90.
[19]Dong Y, Camci E. Faster RRT-based nonholonomic pa-th planning in 2D building environments using skeleton-constrained path biasing [J]. Journal of Intelligent and Robotic Systems, 2018, 89 (3-4): 387-401.
[20]Chen L, Shan Y. A fast and efficient double-tree RRT*-like sampling-based planner applying on mobile robotic systems [J]. IEEE-Asme Transactions on Mecha-tronics, 2018, 23 (6): 2568-2578.
[21]Tahir Z, Qureshi A H. Potentially guided bidirection-alized RRT* for fast optimal path planning in cluttered environments [J]. Robotics and Autonomous Systems, 2018, 108 (1): 13-27.
[22]Zhang Z, Wu D F. A path-planning strategy for unman-ned surface vehicles based on an adaptive hybrid dyna-mic step size and target attractive force-RRT algorithm [J]. Journal of Marine Science and Engineering, 2019, 7 (5): 132-141.
[23]Astol A. Exponential stabilization of a wheeled mobile robot via discontinuous control [J]. Journal of Dynamic Systems Measurement and Control, 1999, 121 (1): 121-126.
[24]Hu B. An efficient RRT-based framework for planning short and smooth wheeled robot motion under kinodynamic constraints [J]. IEEE Transactions on Industrial Electronics, 2020, 68 (4): 3292-3302.
[25]Palmieri L G. A novel RRT extend function for ecient and smooth mobile robot motion planning[C]//IEEE/RSJ International Conference on Intelligent Robots and Systems. Chicago, USA, 2014: 205-211. |
|
|
|