Xiao Jinzhuang, Yu Xuele, Sun Keke, Zhou Zhen, Zhou Gang
College of Electronic Information Engineering, Hebei University, Baoding 071000, China.
Math Biosci Eng. 2022 Aug 26;19(12):12532-12557. doi: 10.3934/mbe.2022585.
With their intelligence, flexibility, and other characteristics, automated guided vehicles (AGVs) have been popularized and promoted in traditional industrial markets and service industry markets. Compared with traditional transportation methods, AGVs can effectively reduce costs and improve the efficiency of problem solving in various application developments, but they also lead to serious path-planning problems. Especially in large-scale and complex map environments, it is difficult for a single algorithm to plan high-quality moving paths for AGVs, and the algorithm solution efficiency is constrained. This paper focuses on the indoor AGV path-planning problem in large-scale, complex environments and proposes an efficient path-planning algorithm (IACO-DWA) that incorporates the ant colony algorithm (ACO) and dynamic window approach (DWA) to achieve multiobjective path optimization. First, inspired by the biological population level, an improved ant colony algorithm (IACO) is proposed to plan a global path for AGVs that satisfies a shorter path and fewer turns. Then, local optimization is performed between adjacent key nodes by improving and extending the evaluation function of the traditional dynamic window method (IDWA), which further improves path security and smoothness. The results of simulation experiments with two maps of different scales show that the fusion algorithm shortens the path length by 9.9 and 14.1% and reduces the number of turns by 60.0 and 54.8%, respectively, based on ensuring the smoothness and safety of the global path. The advantages of this algorithm are verified. QBot2e is selected as the experimental platform to verify the practicability of the proposed algorithm in indoor AGV path planning.
凭借其智能性、灵活性等特点，自动导引车（AGV）已在传统工业市场和服务业市场得到推广。与传统运输方式相比，AGV能有效降低成本并提高各种应用开发中问题解决的效率，但也引发了严重的路径规划问题。尤其是在大规模、复杂地图环境中，单一算法难以规划出高质量的AGV移动路径，算法求解效率受到限制。本文聚焦于大规模、复杂环境下的室内AGV路径规划问题，提出一种高效路径规划算法（IACO-DWA），该算法融合蚁群算法（ACO）和动态窗口方法（DWA）以实现多目标路径优化。首先，受生物种群层面启发，提出一种改进蚁群算法（IACO）为AGV规划满足路径较短且转弯较少的全局路径。然后，通过改进和扩展传统动态窗口方法（IDWA）的评价函数在相邻关键节点间进行局部优化，进一步提高路径安全性和平滑性。对两张不同比例尺地图的仿真实验结果表明，该融合算法在保证全局路径平滑性和安全性的基础上，路径长度分别缩短了9.9%和14.1%，转弯次数分别减少了60.0%和54.8%。验证了该算法的优势。选取QBot2e作为实验平台，验证所提算法在室内AGV路径规划中的实用性。
