Abstract
This research introduces a path planning method based on the geometric A-star algorithm. The whole approach is applied to an Automated Guided Vehicle (AGV) in order to avoid the problems of many nodes, long-distance and large turning angle, and these problems usually exist in the sawtooth and cross paths produced by the traditional A-star algorithm. First, a grid method models a port environment. Second, the nodes in the close-list are filtered by the functions $P\left ({{x,y} }\right)$ and $W\left ({{x,y} }\right)$ and the nodes that do not meet the requirements are removed to avoid the generation of irregular paths. Simultaneously, to enhance the stability of the AGV regarding turning paths, the polyline at the turning path is replaced by a cubic B-spline curve. The path planning experimental results applied to different scenarios and different specifications showed that compared with other seven different algorithms, the geometric A-star algorithm reduces the number of nodes by 10% ~ 40%, while the number of turns is reduced by 25%, the turning angle is reduced by 33.3%, and the total distance is reduced by 25.5%. Overall, the simulation results of the path planning confirmed the effectiveness of the geometric A-star algorithm.
Highlights
Recent years, with the rapid development of warehouse logistics automation and the increase of labor cost, the transportation of goods in many areas is developing towards systematization and automatisation
A series of algorithms, such as particle swarm optimization (PSO) algorithm [17], genetic algorithm (GA) [18], and ant colony (AC) algorithm [19] have been applied to path search processes
This paper introduces a new path planning algorithm, socalled geometric A-star algorithm, and whose objective is to improve the irregular path generated by the common A-star algorithm
Summary
With the rapid development of warehouse logistics automation and the increase of labor cost, the transportation of goods in many areas is developing towards systematization and automatisation. A series of algorithms, such as particle swarm optimization (PSO) algorithm [17], genetic algorithm (GA) [18], and ant colony (AC) algorithm [19] have been applied to path search processes They are generally easy to implement, they are generally computationally expensive and often fall into some local optimum, so they are not always appropriate for path planning in complex grid map environments. Fu et al [28] proposed an improved A-star algorithm to shorten the path by judging whether there is an obstacle between the current way point and the target point This method is computationally expensive and does not smooth the turning path. The proposed approach generates a grid map of the port environment so that the A-star algorithm can quickly search. The last section summarizes the optimization effect of the geometric A-star algorithm and the shortcomings of current research and a few directions for future research
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