Yang Guoliang, Xiong Wenkai
School of Electrical Engineering and Automation, Jiangxi University of Science and Technology, Ganzhou, 341000, Jiangxi, China.
Sci Rep. 2024 Sep 18;14(1):21757. doi: 10.1038/s41598-024-72530-9.
This paper proposes an improved hybrid algorithm for automated guided vehicles (AGVs) in port environments based on the concept of key obstacles for the JPS and DWA algorithms. Given the complexity of the port environment and the abundance of obstacles, the traditional heuristic function of the JPS algorithm is improved by adding the key obstacle heuristic function. Simultaneously, improvements are made to the evaluation function of the traditional DWA algorithm, where the braking distance is segmented into key obstacle distance and non-key obstacle distance, utilizing the concept of key obstacles. Simulation experiments are conducted using Matlab to demonstrate the effectiveness of the improved algorithm. Moreover, the performance of the hybrid algorithm is compared with five mainstream algorithms in a real simulated port environment, and the final results show the significant enhancement of this paper's algorithm in several key performance metrics. Thus, this study provides a feasible strategy for improved path planning efficiency for AGV in the port environment.
本文基于JPS算法和DWA算法的关键障碍物概念，提出了一种用于港口环境中自动导引车（AGV）的改进混合算法。鉴于港口环境的复杂性和障碍物的多样性，通过添加关键障碍物启发式函数对JPS算法的传统启发式函数进行了改进。同时，利用关键障碍物的概念，对传统DWA算法的评估函数进行了改进，将制动距离分为关键障碍物距离和非关键障碍物距离。使用Matlab进行了仿真实验，以证明改进算法的有效性。此外，在真实的模拟港口环境中，将混合算法的性能与五种主流算法进行了比较，最终结果表明本文算法在几个关键性能指标上有显著提升。因此，本研究为提高港口环境中AGV的路径规划效率提供了一种可行的策略。
