Development of Path Planning Algorithm Using Probabilistic Roadmap Based on Modified Ant Colony Optimization

Firas A. Raheem, Mohammed I. Abdulkareem
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引用次数: 3

Abstract

In this paper, a unique combination among probabilistic roadmap, modified ant colony optimization, and third order B-spline curve has been proposed to solve path planning problems in complex and very complex environments. This proposed approach can be divided into three stages. First stage involves constructing a random roadmap depending on the environment complexity using probabilistic roadmap algorithm. Roadmap can be constructed by distributing N nodes randomly in complex and very complex static environments then pairing these nodes together according to some criteria or conditions. The constructed roadmap contains a huge number of possible random paths that may lead to connecting the start and the goal points together. Second stage includes finding path within the pre-constructed roadmap. Modified ant colony optimization has been proposed to find or to search the best path between start and goal points, where in addition to the proposed combination, ACO has been modified to increase its ability to find shorter path. Finally, the third stage uses B-spline curve to smooth and reduce the total length of the found path in the previous stage. The results of the proposed approach ensure the feasible path between start and goal points in complex and very complex environments. Also, the path is guaranteed to be short, smooth, continuous and safe.
基于改进蚁群优化的概率路线图路径规划算法的发展
本文提出了一种将概率路线图、改进蚁群优化和三阶b样条曲线相结合的独特方法来解决复杂和极复杂环境下的路径规划问题。该方法可分为三个阶段。第一阶段采用概率路线图算法,根据环境复杂度构建随机路线图。路线图可以通过在复杂和非常复杂的静态环境中随机分布N个节点,然后根据一些标准或条件将这些节点配对在一起来构建。构建的路线图包含大量可能的随机路径,这些路径可能将起点和目标点连接在一起。第二阶段包括在预先构建的路线图中寻找路径。提出了一种改进的蚁群优化算法来寻找或搜索起始点和目标点之间的最佳路径,其中除了提出的组合外,还对蚁群算法进行了改进,以提高其寻找更短路径的能力。最后,第三阶段使用b样条曲线对前一阶段找到的路径进行平滑和减少总长度。该方法的结果保证了在复杂和非常复杂的环境中起始点和目标点之间的可行路径。同时保证了路径的短、顺、连续和安全。
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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