融合改进A*与DWA算法的车间移动机器人路径规划

doi:10.13229/j.cnki.jdxbgxb.20250381

摘要/Abstract

摘要：

为实现复杂车间环境下移动机器人高效路径规划，提出了一种融合改进A^*与DWA算法的混合路径规划方法。首先，优化全局路径规划A^*算法，考虑障碍物密度、转弯次数和运动惯性因子的影响，改进启发函数；其次，引入权重因子动态调整启发函数权重，采用Douglas-Peucker算法简化路径，同时引入B-Spline算法平滑路径；最后，改进局部路径规划DWA算法，将全局最优路径关键点作为DWA算法过程的目标点，引入当前点与目标点距离、当前点与动态障碍物距离和路径跟踪代价3项评价子项，重构轨迹评价函数。仿真结果表明：相较于传统A^*算法，改进A^*算法在路径长度、规划时间、扩展节点数和拐点数方面分别减少15.80%、81.20%、2.30%和74.10%；在添加临时静态障碍物和动态障碍物场景中，融合改进算法的运行时间分别缩短22.02%和22.32%。试验结果表明：融合改进算法可高效规划路径且安全避开障碍物。

关键词: 车间移动机器人, 路径规划, 动态避障, A^*算法, DWA算法

Abstract:

To achieve efficient path planning for mobile robots in complex workshop environments， a hybrid path planning method integrating improved A^* algorithm and the DWA algorithm was proposed. Firstly， the global path planning A^* algorithm was optimized by improving the heuristic function， taking into account the influences of obstacle density， the number of turns， and motion inertia factors. Secondly， weight factors were introduced to dynamically adjust the heuristic function weights， the Douglas-Peucker algorithm was employed to simplify the path， and the B-Spline algorithm was applied to smooth it. Finally， the local path planning DWA algorithm was improved， the key points of the globally optimal path were used as target points in the DWA alogorithmprocess. A trajectory evaluation function was reconstructed by introducing three evaluation terms： the distance between the current point and the target point， the distance between the current point and dynamic obstacles， and the path tracking cost. Simulation results showed that， compared with the traditional A^* algorithm， the improved A^* algorithm reduced the path length， planning time， number of expanded nodes， and number of turning points by 15.80%， 81.20%， 2.30%， and 74.10%， respectively. In scenarios involving temporary static obstacles and dynamic obstacles， the runtime of the integrated improved algorithm was reduced by 22.02% and 22.32%， respectively. Experimental results demonstrated that the integrated improved algorithm enabled efficient path planning and safe obstacle avoidance.

Key words: workshop mobile robot, path planning, dynamic obstacle avoidance, A* algorithm, DWA algorithm

中图分类号:

TP242

张伏,韩伟东,鲍若飞,张亚坤,王亚飞,付三玲. 融合改进A^*与DWA算法的车间移动机器人路径规划[J]. 吉林大学学报(工学版), 2025, 55(9): 3020-3031.

Fu ZHANG,Wei-dong HAN,Ruo-fei BAO,Ya-kun ZHANG,Ya-fei WANG,San-ling FU. Path planning of workshop mobile robots integrated with improved A^* and DWA algorithms[J]. Journal of Jilin University(Engineering and Technology Edition), 2025, 55(9): 3020-3031.

图/表 21

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表1

权重动态调整结果"

$h (n)$	$h (n) h s t a r t$	$1 - h (n) h s t a r t$	$w (n)$
100	1.00	0.00	3.00
75	0.75	0.25	1.94
50	0.50	0.50	1.45
25	0.25	0.75	1.21
0	0.00	1.00	1.10

表1

图5

图6

图7

图8

表2

图9

图10

图11

图12

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图14

图15

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图19

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算法	长度/m	规划时间/s	扩展节点数/个	拐点数/个
传统A^*	131.17	2.61	4 500	27
Dijkstra	139.94	1.98	5 477	13
改进A^*	110.43	0.49	4 398	7