改进A^(*)与ROA-DWA融合的机器人路径规划  

Path planning based on fusion of improved A^(*) and ROA-DWA for robot

作　　者：刘宇庭 郭世杰[1] 唐术锋[1] 张学炜[1,2] 李田田 LIU Yuting;GUO Shijie;TANG Shufeng;ZHANG Xuewei;LI Tiantian(School of Mechanical Engineering,Inner Mongolia University of Technology,Hohhot 010051,China;School of Mechanical Engineering,Zhejiang University,Hangzhou 310058,China)

机构地区：[1]内蒙古工业大学机械工程学院,内蒙古呼和浩特010051 [2]浙江大学机械工程学院,浙江杭州310058

出　　处：《浙江大学学报（工学版）》2024年第2期360-369,共10页Journal of Zhejiang University：Engineering Science

基　　金：国家自然科学基金资助项目(52065053);科技部国家重点研发计划资助项目(2018YFB1307501);中央引导地方科技发展专项资助项目(2020ZY0002);内蒙古关键技术攻关资助项目(2020GG0255);内蒙古自然科学基金资助项目(2022FX01,2023LHMS05018);内蒙古自治区高等学校科学研究资助项目(NJZY21308);内蒙古自治区直属高校基本科研业务费资助项目(JY20220046);内蒙古自治区高等学校青年科技英才支持计划资助项目(NJYT23043);内蒙古自治区高等学校创新团队发展支持计划资助项目(NMGIRT2213);内蒙古自治区科技计划资助项目(2021GG0259)。

摘　　要：为了解决机器人路径规划中传统A^(*)算法和动态窗口法(DWA)存在的遍历节点较多、冗余点较多以及路径不平滑,缺乏全局引导,易陷入局部最优以及安全性低等问题,提出融合改进A^(*)算法和随机避障动态窗口法(ROA-DWA)的路径规划算法.该算法通过启发式函数的权重调整、Floyd算法、冗余点删除策略、静态和动态障碍物分类处理和速度自适应因子等方式来提高搜索效率,减少路径长度和拐点数量,将已知障碍物对路径的影响最小化,大幅提高动态避障效率,使得机器人在平稳到达目标点的同时还提升了机器人的安全性,更好地适应复杂的动态和静态环境.实验结果表明,该算法具有较好的全局最优性和局部避障能力,在大型地图中展现出更好的优势.A path planning algorithm based on the fusion of the improved A^(*)algorithm and the random obstacle avoidance dynamic window method(ROA-DWA)was proposed in order to address the issues of excessive traversal nodes,redundant points,non-smooth paths,lack of global guidance,susceptibility to local optima,and low safety in traditional A^(*)algorithm and dynamic window approach(DWA)for robot path planning.The search efficiency was improved by adjusting the weights of heuristic functions,Floyd’s algorithm,redundant point deletion strategy,static and dynamic obstacle classification,and speed adaptive factor.The length of the path and the number of inflection points were reduced,and the influence of known obstacles on the path was minimized to improve the efficiency of dynamic obstacle avoidance,which enabled the robot to smoothly arrive at the target point and improved the safety of the robot,and better adapted to complex dynamic and static environments.The experimental results show that the algorithm has better global optimality and local obstacle avoidance ability,and shows better advantages in large maps.

关 键 词：机器人路径规划 动态避障 改进A^(*)算法 随机避障动态窗口法(ROA-DWA) 融合算法 

分 类 号：TP242[自动化与计算机技术—检测技术与自动化装置]