摘要
针对移动机器人路径规划算法不能根据环境自适应调整步长的问题,提出一种基于环境复杂度的变步长路径规划算法。以快速搜索随机树(RRT)算法为例,引入衡量路径规划性能的参数,通过遗传算法寻找最优步长与环境复杂度之间的关系,建立最优步长与环境复杂度的函数表达式。针对局部环境的特殊性,提出基于滑动窗的变步长RRT路径规划算法。基于该算法,移动机器人能够根据实时局部环境动态改变路径规划的搜索步长,提高了算法的整体性能。最后通过Matlab仿真实验验证了所提出的RRT算法较传统RRT算法具有高效、平稳、代价小的优点。
Given the problem that the path planning algorithm of a mobile robot cannot adjust the step size adaptively according to the environment, a variable step size path planning algorithm based on the complexity of the environment is proposed. A rapidly-exploring random tree (RRT) algorithm is taken as an example. First, the parameters measuring the path planning performance were introduced, and the relationship between the optimal step size and the environment complexity was found through a genetic algorithm. A functional expression for optimal step size and environmental complexity was then established. By taking account of the particularity of the local environment, a variable step size RRT path planning algorithm based on a sliding window is proposed. Based on this algorithm, the mobile robot can dynamically change the search step according to the real-time local environment in the path planning process, and improve the overall performance of the algorithm. Finally, Matlab simulation experiments verified that the variable step size RRT algorithm has the advantages of high efficiency, high stability and low cost compared with the traditional RRT algorithm.
作者
康博涵
黄静雯
KANG BoHan;HUANG JingWen(College of Information Science and Technology,Beijing University of Chemical Technology,Beijing 100029;School of Logistics,Beijing Wuzi University,Beijing 101149,China)
出处
《北京化工大学学报（自然科学版）》
CAS
CSCD
北大核心
2023年第4期87-93,共7页
Journal of Beijing University of Chemical Technology(Natural Science Edition)
基金
国家自然科学基金(11972070/11702016)。
关键词
移动机器人
路径规划
快速搜索随机树算法
遗传算法
滑动窗
环境复杂度
mobile robot
path planning
rapidly-exploring random tree algorithm
genetic algorithm
sliding window
environment complexity
