Configuration Optimization of a Dual-Arm Reconfigurable Space Robot Based on Closed-Chain Inertia Matching

IF 6.4 2区 计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS IEEE Transactions on Automation Science and Engineering Pub Date : 2024-08-23 DOI:10.1109/TASE.2024.3445453
Zhihui Xue;Jinguo Liu;Yangmin Li;Junsen Liu
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Abstract

The dual-arm space robot usually forms a closed-chain constraint system with a target through collaborative operations when performing tasks. Most previous related research has focused on the performance of open-chain robots themselves. Studying the manipulation performance of a closed-chain robot system is of great significance. This article proposes a reconfigurable space robot (RSR) system for on-orbit servicing. A graph theory based framework for the automatic generation of reconfigurable robot models is proposed to address the characteristics of variable topology structures. Meanwhile, a new index – the closed-chain inertia matching index is proposed to evaluate its configuration effectively. Compared with traditional dynamic manipulability ellipsoid (DME) and manipulating force ellipsoid (MFE), the effectiveness of the proposed closed-chain inertia matching ellipsoid (IME) is verified. Compared with the DME and MFE, the IME effectively considers the influence of load and can effectively express the dynamic torque force/torque transmission efficiency from the joint actuator to the load in a closed-chain system. The IME can not only be used to determine the optimal joint configuration under a specific closed-chain configuration, but also to determine the optimal nonisomorphic configuration of a reconfigurable robot. Finally, the results of configuration optimization of the closed-chain dual-arm reconfigurable space robot are given.Note to Practitioners—This paper aims to study the determination of the optimal configuration for the RSR during task execution. Due to its various configurations, the RSR has a complex modeling process. This article proposes a modeling framework for automatically generating RSR models that can effectively achieve automatic modeling of various configurations. In addition, most of the previous related research focused on the performance of open-chain robots themselves, ignoring the impact of load. This article proposes a new closed-chain system performance indicator to evaluate the robot configuration. The proposed evaluation indicator effectively considers the influence of load, which is of practical significance to improve the efficiency of task execution. The effectiveness of the proposed index in determining the optimal configuration is demonstrated by simulation verification. In the future, we will focus on multi-arm collaborative operations under a specific configuration.
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基于闭链惯性匹配的双臂可重构太空机器人配置优化
双臂空间机器人在执行任务时,通常通过协同操作与目标形成一个闭环约束系统。以往的相关研究大多集中在开链机器人本身的性能上。研究闭链机器人系统的操作性能具有重要意义。提出了一种用于在轨服务的可重构空间机器人系统。针对可变拓扑结构的特点,提出了一种基于图论的可重构机器人模型自动生成框架。同时,提出了一种新的指标——闭链惯性匹配指标来有效地评价其构型。通过与传统的动态可操纵性椭球(DME)和操纵力椭球(MFE)的比较,验证了所提出的闭链惯性匹配椭球(IME)的有效性。与DME和MFE相比,IME有效地考虑了负载的影响,能够有效地表达闭环系统中关节执行器到负载的动态转矩力/转矩传递效率。该方法不仅可用于确定特定闭链构型下的最优关节构型,也可用于确定可重构机器人的最优非同构构型。最后给出了闭链双臂可重构空间机器人的构型优化结果。从业人员注意事项:本文旨在研究在任务执行过程中确定RSR的最佳配置。由于其各种配置,RSR具有复杂的建模过程。本文提出了一种自动生成RSR模型的建模框架,可以有效地实现各种配置的自动建模。此外,以往的相关研究大多集中在开链机器人本身的性能上,忽略了负载的影响。本文提出了一种新的闭环系统性能指标来评价机器人的构型。所提出的评价指标有效地考虑了负载的影响,对提高任务执行效率具有实际意义。仿真验证了该指标在确定最优构型方面的有效性。未来,我们将专注于特定配置下的多臂协同作战。
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来源期刊
IEEE Transactions on Automation Science and Engineering
IEEE Transactions on Automation Science and Engineering 工程技术-自动化与控制系统
CiteScore
12.50
自引率
14.30%
发文量
404
审稿时长
3.0 months
期刊介绍: The IEEE Transactions on Automation Science and Engineering (T-ASE) publishes fundamental papers on Automation, emphasizing scientific results that advance efficiency, quality, productivity, and reliability. T-ASE encourages interdisciplinary approaches from computer science, control systems, electrical engineering, mathematics, mechanical engineering, operations research, and other fields. T-ASE welcomes results relevant to industries such as agriculture, biotechnology, healthcare, home automation, maintenance, manufacturing, pharmaceuticals, retail, security, service, supply chains, and transportation. T-ASE addresses a research community willing to integrate knowledge across disciplines and industries. For this purpose, each paper includes a Note to Practitioners that summarizes how its results can be applied or how they might be extended to apply in practice.
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