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A new genetic algorithm approach to smooth path planning for mobile robots

机译:遗传算法的移动机器人平滑路径规划

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摘要

Purpose - The purpose of this paper is to consider the smooth path planning problem for a mobile robot based on the genetic algorithm (GA) and the Bezier curve. Design/methodology/approach - The workspace of a mobile robot is described by a new grid-based representation that facilitates the operations of the adopted GA. The chromosome of the GA is composed of a sequence of binary numbered grids (i.e. control points of the Bezier curve). Ordinary genetic operators including crossover and mutation are used to search the optimum chromosome where the optimization criterion is the length of a piecewise collision-free Bezier curve path determined by the control points. Findings - This paper has proposed a new smooth path planning for a mobile robot by resorting to the GA and the Bezier curve. A new grid-based representation of the workspace has been presented, which makes it convenient to perform operations in the GA. The GA has been used to search the optimum control points that determine the Bezier curve-based smooth path. The effectiveness of the proposed approach has been verified by a numerical experiment, and some performances of the obtained method have also been analyzed. Research limitations/implications - There still remain many interesting topics, for example, how to solve the specific smooth path planning problem by using the GA and how to promote the computational efficiency in the more grids case. These issues deserve further research. Originality/value - The purpose of this paper is to improve the existing results by making the following three distinctive contributions: a rigorous mathematical formulation of the path planning optimization problem is formulated; a general grid-based representation (2n × 2n) is proposed to describe the workspace of the mobile robots to facilitate the implementation of the GA where n is chosen according to the trade-off between the accuracy and the computational burden; and the control points of the Bezier curve are directly linked to the optimization criteria so that the generated paths are guaranteed to be optimal without any need for smoothing afterwards.
机译:目的-本文的目的是考虑基于遗传算法(GA)和Bezier曲线的移动机器人的平滑路径规划问题。设计/方法/方法-移动机器人的工作空间由新的基于网格的表示形式描述,该表示形式便于所采用的GA的操作。 GA的染色体由一系列二进制编号的网格(即Bezier曲线的控制点)组成。使用常规遗传算子(包括交叉和突变)来搜索最佳染色体,其中优化标准是由控制点确定的分段无碰撞贝塞尔曲线路径的长度。发现-本文通过利用GA和Bezier曲线为移动机器人提出了一种新的平滑路径规划。提出了基于工作空间的新的基于网格的表示形式,这使得在GA中执行操作变得很方便。 GA已用于搜索确定基于Bezier曲线的平滑路径的最佳控制点。通过数值实验验证了该方法的有效性,并分析了该方法的一些性能。研究局限性/含义-仍然存在许多有趣的主题,例如,如何通过使用遗传算法解决特定的平滑路径规划问题以及如何在更多网格情况下提高计算效率。这些问题值得进一步研究。原创性/价值-本文的目的是通过做出以下三个独特的贡献来改善现有的结果:制定严格的路径规划优化问题的数学公式;提出了一种通用的基于网格的表示形式(2n×2n)来描述移动机器人的工作空间,以促进GA的实现,其中根据精度和计算负担之间的权衡取舍选择n; Bezier曲线的控制点和优化标准直接链接在一起,因此可以确保生成的路径是最佳的,而无需随后进行平滑处理。

著录项

  • 来源
    《Assembly Automation》 |2016年第2期|138-145|共8页
  • 作者单位

    College of Electrical Engineering and Automation, Shandong University of Science and Technology, Qingdao, China;

    College of Electrical Engineering and Automation, Shandong University of Science and Technology, Qingdao, China and Department of Computer Science, Brunel University London, Uxbridge, Middlesex, UK;

    College of Information and Control Engineering, China University of Petroleum (East China), Qingdao, China;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Robotics; Genetic algorithms;

    机译:机器人技术遗传算法;

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