Human-Inspired Robotic Assembly for Multiple Peg-In/Out-Hole Tasks in On-Orbit Refueling

IF 5.3 2区 计算机科学 Q2 ROBOTICS IEEE Robotics and Automation Letters Pub Date : 2025-01-30 DOI:10.1109/LRA.2025.3536298
Rui Zhang;Qiang Zhang;Xiaodong Zhou
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Abstract

On-orbit refueling technology requires using robots with multiple peg-in-hole and peg-out-hole capabilities. However, complex contact conditions can cause jamming, thus posing significant challenges to automated refueling. To address this shortcoming, this letter proposes a human-inspired multiple peg-in/out-hole assembly method. The proposed method integrates a variable admittance force controller based on a non-diagonal stiffness matrix and a strategy for handling multiple peg-in/out-hole operations. In addition, by coupling the position and orientation stiffness, a robot's adaptability in dynamic assembly environments is significantly enhanced. Moreover, the proposed method enables autonomous posture adjustment based on real-time force sensor data and allows a robot to retry operations in case of jamming, thus eliminating the need for complex motion trajectory planning. The results of the ground refueling experiments show that the proposed method can successfully complete the multiple peg-in/out-hole tasks and effectively resist external interference. The proposed method could be of valuable reference significance for on-orbit refueling tasks.
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基于人的在轨加注多挂入/出孔机器人装配
在轨加油技术需要使用具有多个钉入孔和钉出孔能力的机器人。然而,复杂的接触条件可能导致干扰,从而对自动加油提出了重大挑战。为了解决这一缺点,本文提出了一种人性化的多钩入/外孔组合方法。该方法集成了基于非对角刚度矩阵的可变导纳力控制器和处理多次钉入/出孔操作的策略。此外,通过位置刚度和方向刚度的耦合,可以显著提高机器人对动态装配环境的适应性。此外,该方法能够基于实时力传感器数据自主调整姿态,并允许机器人在遇到干扰时重试操作,从而无需进行复杂的运动轨迹规划。地面加注实验结果表明,该方法能较好地完成多个挂入/出孔任务,并能有效地抵抗外界干扰。该方法对在轨加油任务具有重要的参考意义。
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来源期刊
IEEE Robotics and Automation Letters
IEEE Robotics and Automation Letters Computer Science-Computer Science Applications
CiteScore
9.60
自引率
15.40%
发文量
1428
期刊介绍: The scope of this journal is to publish peer-reviewed articles that provide a timely and concise account of innovative research ideas and application results, reporting significant theoretical findings and application case studies in areas of robotics and automation.
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