A Robotic Path Planning by Using Crow Swarm Optimization Algorithm

Mohammed Yousif, A. Salim, Wisam K. Jummar
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引用次数: 4

Abstract

: One of the most common problem in the design of robotic technology is the path planning. The challenge is choosing the robotics’ path from source to destination with minimum cost. Meta-heuristic algorithms are popular tools used in a search process to get optimal solution. In this paper, we used Crow Swarm Optimization (CSO) to overcome the problem of choosing the optimal path without collision. The results of CSO compared with two meta-heuristic algorithms: PSO and ACO in addition to a hybrid method between these algorithms. The comparison process illustrates that the CSO better than PSO and ACO in path planning, but compared to hybrid method CSO was better whenever the smallest population. Consequently, the importance of research lies in finding a new method to use a new meta-humanistic algorithm to solve the problem of robotic path planning.
基于群算法的机器人路径规划
路径规划是机器人技术设计中最常见的问题之一。挑战在于如何以最小的成本选择机器人从源头到目的地的路径。元启发式算法是搜索过程中获得最优解的常用工具。在本文中,我们使用群算法(CSO)来解决无碰撞的最优路径选择问题。将CSO算法与两种元启发式算法:粒子群算法和蚁群算法进行了比较,并在这两种算法之间进行了混合算法。对比结果表明,CSO算法在路径规划方面优于PSO和蚁群算法,但在种群最小时,CSO算法优于混合算法。因此,研究的重点在于寻找一种新的方法,利用新的元人文算法来解决机器人路径规划问题。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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