用于移动机器人的新型自适应路径平滑优化方法

IF 16.4 1区 化学 Q1 CHEMISTRY, MULTIDISCIPLINARY
S. Duan, Lin-Xin Zhang, Xu Han, Yu-Le Li, Fang Wang, G. R. Liu
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引用次数: 0

摘要

 摘要:安全平稳的运行路径是移动机器人完成任务的前提。虽然现有的路径优化方法通过引入贝塞尔曲线对路径的转弯点进行局部优化,提高了规划路径的平滑度,但这些方法大多通过人工选择控制点的位置,并主观分析优化路径的可行性。有人认为这种方法主观性强,选择过程繁琐,并不可取。针对这一不足,本研究提出了一种自适应路径平滑优化方法,将神经网络、遗传算法和贝塞尔曲线相结合,有效解决了控制点选择过程中主观性强、步骤繁琐、效率低等问题。首先,构建与控制点位置和路径偏移量相对应的数据集。根据实际工况,得出控制点位置的值空间。采用拉丁超立方采样法对二阶贝塞尔曲线的控制点位置进行采样,并将其输入贝塞尔曲线求解模型,计算出相应的路径偏移量。由此获得与控制点位置和路径偏移量相对应的数据集。根据数据集,使用神经网络算法对其进行训练,并构建路径偏移的预测模型。随后,参考路径偏移预测模型,综合考虑移动机器人运动安全性和路径平滑性的多种影响因素,制定了性能评估函数。然后引入遗传算法来检测不同环境下的最优控制点。所提出的方法在不同的运行环境下进行了实验验证。研究结果表明,目前提出的自适应路径平滑优化方法与目前流行的方法相比,具有明显的适用性和有效性。它具有快速路径规划、减少路径转折点和理想路径平滑度等优点。此外,它还能通过预设标准确保移动机器人在规划路径上的安全性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Novel Adaptive Path-Smoothening Optimization Method For Mobile Robots
 Abstract: A safe and smooth operating path is a prerequisite for mobile robots to accomplish tasks. Although the existing path optimization methods improve the smoothness of the planned path by introducing Bézier curve to locally optimize the path with regard to turning points, most of these methods manually select the position of control points and subjectively analyze the feasibility of the optimized path. It is argued unfavourably that it exhibits strong subjectivity and cumbersome selection process. To this gap, an adaptive path-smoothening optimization method is proposed in this study, which combines neural network, genetic algorithm, and Bézier curve to effectively resolve the problems of strong subjectivity, cumbersome steps, and thus low efficiency in the selection process of control points. To start with, the data set corresponding to the position of the control point and the path offset are constructed. Based on the actual working conditions, the value space of control point position is derived. Latin hypercube sampling is used to sample the control point position of the second-order Bézier curve, which is input into the Bézier curve solution model to calculate the corresponding path offset. The data set corresponding to the position of control point and path offset are thus acquired. Based on the data set, the neural network algorithm is used to train it, and the prediction model of path offset is constructed. Subsequently, with reference to the prediction model of path offset, a performance evaluation function is formulated by comprehending multiple influential factors of mobile robot motion safety and path smoothness. The genetic algorithm is then introduced to detect the optimal control points in different environments. The proposed method is verified by experiments in different operating environments. The study results show that the currently proposed adaptive path-smoothening optimization method exhibits remarkably superior applicability and effectiveness compared to the currently prevailing methods. It demonstrates advantages of fast path planning, reduced path turning points, and desirable path smoothness. In addition, it can also ensure the safety of mobile robot along the planned path as availed by a pre-set criterion.
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来源期刊
Accounts of Chemical Research
Accounts of Chemical Research 化学-化学综合
CiteScore
31.40
自引率
1.10%
发文量
312
审稿时长
2 months
期刊介绍: Accounts of Chemical Research presents short, concise and critical articles offering easy-to-read overviews of basic research and applications in all areas of chemistry and biochemistry. These short reviews focus on research from the author’s own laboratory and are designed to teach the reader about a research project. In addition, Accounts of Chemical Research publishes commentaries that give an informed opinion on a current research problem. Special Issues online are devoted to a single topic of unusual activity and significance. Accounts of Chemical Research replaces the traditional article abstract with an article "Conspectus." These entries synopsize the research affording the reader a closer look at the content and significance of an article. Through this provision of a more detailed description of the article contents, the Conspectus enhances the article's discoverability by search engines and the exposure for the research.
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