Garage AGV Path Planning and Simulation Based on Improved DWA

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

Abstract

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

CLC Number:

TP391.9

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

Figures/Tables 18

Fig. 1

Table 1

Improved ant colony parameter setting

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

Table 1

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Table 2

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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