摘要
针对A^(*)算法在移动机器人路径规划存在搜索效率低,路径斜穿障碍物顶点,路径拐弯多等问题。提出一种改进的A^(*)算法,首先在A^(*)算法的邻域扩展中采用避免斜穿障碍物顶点的策略;再引入障碍物因素对评价函数进行指数加权,减少不必要的搜索,提高A^(*)算法的效率和灵活性,使算法偏向于选择障碍物较少的路径;最后使用三次优化折线的策略,加入障碍物安全距离,减少路径上的冗余节点和拐弯。使用MATLAB进行实验仿真,结果表明,在20 m×20 m、40 m×40 m、60 m×60 m栅格地图环境下,改进A^(*)算法较传统A^(*)算法,搜索时间分别减少70.12%、84.31%、91.44%,扩展节点分别减少53.77%、71.20%、74.30%,路径累计拐弯角度分别减少70.48%、76.31%、82.18%,改进A^(*)算法能够有效的提高移动机器人路径规划的效率,路径更为平滑和安全,且在复杂环境中优势更为明显。
An improved A^(*)algorithm is proposed to address the issues of low search efficiency,path diagonally crossing obstacle vertices,and excessive turns in mobile robot path planning.Firstly,a strategy is introduced to avoid diagonally crossing obstacle vertices during the neighborhood expansion in the A^(*)algorithm.Secondly,an exponential weight is applied to the evaluation function based on obstacle factors to reduce unnecessary search and improve the efficiency and adaptability of the A^(*)algorithm,favoring paths with fewer obstacles.Finally,a three-phase optimization strategy is employed,considering the obstacle safety distance,to minimize redundant nodes and turns in the path.MATLAB simulations are conducted in grid maps of sizes 20×20 m,40×40 m,and 60×60 m.The results demonstrate that the improved A^(*)algorithm significantly reduces search time by 70.12%,84.31%,and 91.44%,respectively,and reduces the number of expanded nodes by 53.77%,71.20%,and 74.30%,respectively.Moreover,the accumulated turning angles in the path are reduced by 70.48%,76.31%,and 82.18%,respectively.The improved A^(*)algorithm effectively enhances the efficiency of mobile robot path planning,resulting in smoother and safer paths,especially in complex environments.
作者
熊勇刚
李波
姚焘
付茂林
李城炫
Xiong Yonggang;Li Bo;Yao Tao;Fu Maolin;Li Chengxuan(College of Mechanical Engineering,Hunan University of technology,Zhuzhou 412007,China)
出处
《电子测量技术》
北大核心
2024年第5期31-36,共6页
Electronic Measurement Technology
基金
湖南省自然科学基金(2022JJ50078)
教育部创新基金(2021JQR026)项目资助。
