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GWO‑Potential Field Method for Mobile Robot Path Planning and Navigation Control

机译:GWO - 移动机器人路径规划和导航控制的潜在现场方法

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摘要

A navigation control methodology is proposed in this paper, which utilizes a hybrid concept of grey wolves optimisation(GWO) algorithm and an artificial potential field (APF) method for on-time path planning of mobile robot. The proposedmethodology runs in two folds. The first fold defines the focus region (FR), which shows the obstacle-free locations of theall possible robot movements. However, in the second fold step GWO algorithm searches the shortest path by minimizing theartificial potential field value of the location generated within FR. The presented methodology was simulated and verified invarious real and simulation environments. The simulation and experimental results reveal that the presented methodologycan not only be capable of finding an optimal or near-optimal robot-path in a complex obstacle-present environment but alsoprovides an effective path planning strategy on-time basis. The results show that the proposed method has out-performedto shorten the path length as well as ensured collision-free navigation. At the same time, virtual intermediate targets (VITs)makes the navigation free from any dead-end situation, even in a cluttered environment.
机译:本文提出了一种导航控制方法,它利用灰狼优化的混合概念(GWO)算法和移动机器人的接通时间路径规划的人工势场(APF)方法。提议方法在两倍中运行。第一折叠定义了焦点区域(FR),其显示了无障碍物的位置所有可能的机器人运动。但是,在第二个折叠步骤中,GWO算法通过最小化最小化路径来搜索最短路径在FR内产生的位置的人工势域值。模拟和验证所提出的方法各种实际和仿真环境。模拟和实验结果表明,呈现的方法不仅能够在复杂的障碍物 - 当前环境中找到最佳或近最佳的机器人路径,还可以在复杂的障碍物 - 当前环境中找到最佳或近最佳的机器人路径准时提供有效的路径规划战略。结果表明,该方法已经出局了缩短路径长度以及确保无碰撞导航。同时,虚拟中间目标(VITS)即使在一个凌乱的环境中,也使导航免于任何死锁情况。

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