基于改进动态窗口的车库AGV路径规划及仿真

doi:10.16182/j.issn1004731x.joss.23-0638

摘要/Abstract

摘要：

针对在智能车库环境下，移动机器人(AGV)在作业过程中面临着复杂路径环境下的路径规划和实时避障，提出一种改进的蚁群算法与动态窗口法(DWA)相结合的混合算法。在全局规划中引入自适应调整信息素挥发系数，融合角度参数建立车库方向信息素矩阵，增大目标点引导能力，扩大蚂蚁的方向选择性；在局部规划中设计基于椭圆方程的障碍物距离评价子函数的改进动态窗口法，并提取改进蚁群算法的全局关键节点作为新增的路径评价子函数，在全局路径的约束下，保持动态规划的最优性。仿真结果表明：改进算法较传统算法能够较好地应对突发的动静态障碍物，更符合AGV实际作业过程中在多重环境因素的影响下的动态规划要求。

关键词: 车库AGV, 蚁群算法, 动态窗口法, 动静态避障, 路径规划, 椭圆方程

Abstract:

Aiming at the path planning and real-time obstacle avoidance of AGV in complex path environment of intelligent garage, an improved hybrid algorithm combining ant colony algorithm and dynamic window method is proposed. In the global planning, the adaptive adjustment of pheromone volatilization coefficient and the fusion of angle parameters are introduced to establish the garage direction pheromone matrix to increase the guidance ability of target points, expand the direction selectivity of ants. In the local planning, the improved DWA of the obstacle distance evaluation sub-function based on elliptic equation is designed. By extracting the global path node of the improved ant colony algorithm as the new path evaluation sub-function, and under the global path constrains, the optimality of dynamic planning is realized. The system simulation results show that the algorithm can reasonably cars deal with the dynamic and static obstacles, more in line with the requirements of dynamic programming under the influence of multiple environmental factors in the actual operation of AGV.

Key words: garage AGV, ant colony, DWA, dynamic and static obstacle avoidance, path planning, elliptic equation

中图分类号:

TP391.9

马宗方,张琳旋,宋琳等 . 基于改进动态窗口的车库AGV路径规划及仿真[J]. 系统仿真学报, 2024, 36(10): 2265-2276.

Ma Zongfang,Zhang Linxuan,Song Lin,et al . Garage AGV Path Planning and Simulation Based on Improved DWA[J]. Journal of System Simulation, 2024, 36(10): 2265-2276.

图/表 18

图1

表1

改进蚁群参数设置

主要参数	量值	主要参数	量值
蚂蚁个数m	50	信息素挥发系数 $ρ$	0.5
最大迭代次数N	200	信息素强度Q	50
信息素启发因子 $α$	1.1	信息素上限	0.9
信息素启发因子 $β$	20	信息素下限	0.1

表1

图2

表2

图3

图4

图5

图6

图7

表3

表4

图8

图9

表5

图10

图11

图12

表6

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栅格地图	算法	最优路径长度/m	迭代次数	拐点数
场景Ⅰ	ACO	31.627	120	12
	EAS	29.799	89	8
	本文算法	28.035	56	7
场景Ⅱ	ACO	32.472	114	10
	EAS	29.201	77	6
	本文算法	27.475	38	3

主要参数	量值
最大线速度/(m/s)	5
最大角速度/(rad/s)	30
最大线加速度/(m/s²)	0.5
最大角加速度/(rad/s²)	60
线速度分辨率/(m/s)	0.1
角速度分辨率/(rad/s)	1
障碍物膨胀半径/m	0.4
机器人半径/m	0.5
时间分辨率/s	0.1

主要参数	量值
方位角评价系数	0.1
障碍物距离评价系数	0.2
速度评价系数	0.3
路径评价系数	0.2

局部规划算法	迭代次数	路径长度/m	运行时间/s
传统DWA	574	31.601	128.465
改进DWA	531	28.758	101.941

局部规划算法	路径长度/m	运行时间/s
传统DWA	32.649	135.728
融合算法	28.435	106.271