PointsBug versus TangentBug algorithm, a performance comparison in unknown static environment

N. Buniyamin, W. Ngah, Z. Mohamad
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引用次数: 6

Abstract

This paper presents an overview of Bug algorithm family local path planning methodology timeline. The Bug algorithm approach detects the nearest obstacle as a mobile robot moves towards a target with limited information about the environment. It uses obstacle border as guidance toward the target. The robot circumnavigates the obstacle till it finds certain condition to fulfill the algorithm criteria to leave the obstacle towards target point. In addition, this paper presents the performance of a new path planning approach, PointsBug algorithm. The performance of PointsBug was compared to TangentBug in term of duration and distance in various types of environment. TangentBug was selected as the algorithm to be compared to as it is the best performing Bug family algorithm that uses a range sensor similar to PointsBug. The outcomes of the research indicates that PointsBug have outperformed TangentBug in average speed in the selected environment as described in this paper.
PointsBug与TangentBug算法,未知静态环境下的性能比较
本文介绍了Bug算法族局部路径规划方法的概述。当移动机器人在环境信息有限的情况下向目标移动时,Bug算法方法可以检测最近的障碍物。它利用障碍物边界作为对目标的引导。机器人绕障碍物飞行,直到找到满足算法准则的条件,使障碍物向目标点移动。此外,本文还介绍了一种新的路径规划方法——PointsBug算法的性能。比较了PointsBug和TangentBug在不同环境下的持续时间和距离。之所以选择TangentBug作为比较算法,是因为TangentBug是Bug族算法中性能最好的,它使用了类似PointsBug的距离传感器。研究结果表明,在本文描述的选定环境中,PointsBug的平均速度优于TangentBug。
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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