A Path Planning Algorithm Based on Improved RRT Sampling Region  

作　　者：Xiangkui Jiang Zihao Wang Chao Dong 

机构地区：[1]College of Automation,Xi’an University of Posts and Telecommunications,Xi’an,710121,China

出　　处：《Computers, Materials & Continua》2024年第9期4303-4323,共21页计算机、材料和连续体（英文）

基　　金：provided by Shaanxi Province’s Key Research and Development Plan(No.2022NY-087).

摘　　要：For the problem of slow search and tortuous paths in the Rapidly Exploring Random Tree(RRT)algorithm,a feedback-biased sampling RRT,called FS-RRT,is proposedbasedon RRT.Firstly,toimprove the samplingefficiency of RRT to shorten the search time,the search area of the randomtree is restricted to improve the sampling efficiency.Secondly,to obtain better information about obstacles to shorten the path length,a feedback-biased sampling strategy is used instead of the traditional random sampling,the collision of the expanding node with an obstacle generates feedback information so that the next expanding node avoids expanding within a specific angle range.Thirdly,this paper proposes using the inverse optimization strategy to remove redundancy points from the initial path,making the path shorter and more accurate.Finally,to satisfy the smooth operation of the robot in practice,auxiliary points are used to optimize the cubic Bezier curve to avoid path-crossing obstacles when using the Bezier curve optimization.The experimental results demonstrate that,compared to the traditional RRT algorithm,the proposed FS-RRT algorithm performs favorably against mainstream algorithms regarding running time,number of search iterations,and path length.Moreover,the improved algorithm also performs well in a narrow obstacle environment,and its effectiveness is further confirmed by experimental verification.

关 键 词：RRT inversive optimization path planning feedback bias sampling mobile robots 

分 类 号：TP39[自动化与计算机技术—计算机应用技术]