摘要
针对在复杂障碍环境下IRRT-Connect算法采样目的性弱、收敛速度慢和路径优化效果差的问题,本文提出了一种依赖环境复杂度的基于IRRT-Connect的自适应路径规划算法。该算法采用IRRT-Connect算法进行初始路径规划以提高初次路径规划效率;其次,该算法引入采样约束概率p对可采样区域进行限制,增强算法采样目的性;最后设计基于环境障碍系数的步长计算方法以实现扩展步长自适应动态调整,增强算法通过复杂环境的能力。通过多组实验对比表明,在复杂环境下,算法节点数减少了9.75%,且路径长度减少了20.82%,规划时间缩短了3.08%,证明本文所改进的IRRT-Connect自适应步长路径规划算法具有较强的适应环境能力,节点利用率高,规划效果更佳。
In response to the challenges of slow convergence and suboptimal path optimization in complex obstacle environments,this paper presents an adaptive path planning algorithm based on IRRT-Connect that is tailored to the environmental complexity.This algorithm combines Informed-RRT and RRT-Connect,namely IRRT-Connect algorithm for initial path planning to improve the efficiency of initial path planning;additionally,it introduces a sampling constraint probability p to confine sampled areas and augment the purposefulness of sampling.Furthermore,a step length calculation method based on environmental obstacle coefficients is devised to dynamically adjust extension step lengths,thereby bolstering the algorithm′s adaptability in traversing complex environments.Through the comparison of multiple sets of experiments,we show that the number of algorithm nodes is reduced by 9.75%,and the path length is reduced by 20.82%,and the planning time is shortened by 3.08%,which proves that the improved IRRT-Connect adaptive step path planning algorithm has a strong ability to adapt to the environment,high node utilization,and the planning effect is better.
作者
马晓群
王昊
刘磊
李树
Ma Xiaoqun;Wang Hao;Liu Lei;Li Shu(Liaoning University of Technology,Jinzhou 121001,China)
出处
《电子测量技术》
北大核心
2024年第15期82-88,共7页
Electronic Measurement Technology
基金
国家自然基金重点项目(62333009)
辽宁省“揭榜挂帅”技术攻关项目(2023JH1/10400092)
辽宁省教育厅面上项目(JYTM20230837)资助。
