变形码垛机器人的多目标轨迹规划与实施

IF 1.8 4区 工程技术 Q3 ENGINEERING, MECHANICAL
Rugui Wang, Ningjuan Zhao, Yichen Dong, Lin Li, Zhipeng Fan
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引用次数: 0

摘要

本文以变形码垛机器人为研究对象,阐述其工作原理并分析其工作轨迹。主要目的是解决机器人运动过程中多目标轨迹规划的复杂难题,重点是最大限度地减少时间、能耗和颠簸。考虑到基于实际工作条件的变换特性,我们提出了优化多目标的一般公式。优化过程采用了具有精英策略的非优势排序遗传算法(NSGA-II),同时将粒子群优化(PSO)集成到优化过程中,以确定特定的变形点。这种方法最终会产生一组帕累托最优解。从这组方案中,选择耗时最少的方案作为多目标规划的最终方案。在配置变换过程中以及在每个配置中,机器人的联合驱动功能都要进行相应的分析。为确保精度,实验中采用的关节驱动函数通过脉冲补偿值进行了微调。随后,进行了实验验证,以验证多目标轨迹规划方法的准确性和实际可行性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Multi-objective trajectory planning and implementation of a metamorphic palletizing robot
This paper focuses on a metamorphic palletizing robot, elaborating on its working principles and analyzing its working trajectory. The primary aim is to address the complex challenge of multi-objective trajectory planning during the robot’s motion, with a focus on minimizing time, energy consumption, and jerk. We present a general formula for optimizing multiple objectives, taking into account transformation characteristics based on actual working conditions. The optimization process employs the Non-dominated Sorting Genetic Algorithm with an elite strategy (NSGA-II), while Particle Swarm Optimization (PSO) is integrated into the optimization progression to identify specific metamorphic points. This approach ultimately produces a set of Pareto optimal solutions. From this set, the solution with the lowest time consumption is chosen as the definitive option for multi-objective planning. The joint driving functions of the robot during configuration transformations and within each configuration are analyzed accordingly. To ensure precision, the joint driving functions employed in the experiment are fine-tuned with pulse compensation values. Subsequently, experimental validation is carried out to verify the accuracy and practical feasibility of the multi-objective trajectory planning method.
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来源期刊
CiteScore
3.80
自引率
10.00%
发文量
625
审稿时长
4.3 months
期刊介绍: The Journal of Mechanical Engineering Science advances the understanding of both the fundamentals of engineering science and its application to the solution of challenges and problems in engineering.
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