A Novel Method for Path Planning of Mobile Robots via Fuzzy Logic and ant Colony Algorithem in Complex Daynamic Environments

A. F. K. purian, B. E. Sadeghian
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Abstract

Researches on mobile robot path planning with meta-heuristic methods to improve classical approaches have grown dramatically in the recent 35 years. Because routing is one of the NP-hard problems, an ant colony algorithm that is a meta-heuristic method has had no table success in this area. In this paper, a new approach for solving mobile robot navigation in dynamic environments, based on the heuristic feature of an optimized ant colony algorithm is proposed. Decision-making influenced by the distances between the origin and destination points and the angle variance to the nearest obstacles. Ideal paths are selected by the fuzzy logic. The proposed ant colony algorithm will optimize the fuzzy rules’ parameters that have been using to On-line (instant) path planning in dynamic environments. This paper presents a new method that can plan local routs all over the area and to guide the moving robot toward the final track. Using this algorithm, mobile robots can move along the ideal path to the target based on the optimal fuzzy control systems in different environments, especially in dynamic and unknown environments.
基于模糊逻辑和蚁群算法的复杂动态环境下移动机器人路径规划新方法
近35年来,基于元启发式方法的移动机器人路径规划研究得到了长足发展。由于路由是np困难问题之一,蚁群算法作为一种元启发式方法在这一领域尚未取得成功。基于优化蚁群算法的启发式特性,提出了一种求解动态环境下移动机器人导航问题的新方法。决策受原点和终点之间的距离以及距离最近障碍物的角度变化的影响。利用模糊逻辑选择理想路径。提出的蚁群算法将对动态环境下在线(即时)路径规划中常用的模糊规则参数进行优化。本文提出了一种新的方法,可以在整个区域内规划局部路线,并引导运动机器人向最终轨道移动。利用该算法,移动机器人可以在不同环境下,特别是在动态和未知环境下,沿着基于最优模糊控制系统的理想路径向目标移动。
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