基于运动意图估计和力前馈补偿的神经导纳控制,用于人机协作

Wenxu Ai, Xinan Pan, Yong Jiang, Hongguang Wang
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摘要

为了增强机器人操纵器对人类伙伴的适应性,并最大限度地减少人机协作中的能量消耗,本文介绍了一种神经导纳控制框架,该框架集成了人类运动意图估计和力前馈补偿。最大似然估计用于推导人机协作中的人类运动意图和刚度,并将其无缝整合到导纳控制中。根据估计的人类意图和刚度,提出了力前馈补偿,以尽量减少交互力和位置跟踪波动。RBF 神经网络控制用于完善位置内环动态,提高位置跟踪精度和响应速度。综合比较模拟验证了该方法在减小人机交互力、提高位置响应速度以及减小交互力和位置波动方面的有效性。在 Franka Emika Panda 机器人平台上进行的实验表明,所提出的方法是有效的,能增强人机协作。
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Neural admittance control based on motion intention estimation and force feedforward compensation for human–robot collaboration

To enhance robotic manipulator adaptation to human partners and minimize human energy consumption in human–robot collaboration, this paper introduces a neural admittance control framework, which integrates human motion intention estimation and force feedforward compensation. Maximum likelihood estimation is employed to derive human motion intention and stiffness within human–robot collaboration, which are seamlessly merged into admittance control. Force feedforward compensation is proposed to minimize interaction force and position tracking fluctuations based on estimated human intention and stiffness. RBF neural network control is used to refine position inner loop dynamics and to improve position tracking accuracy and response speed. Comprehensive comparative simulations validate the method’s effectiveness in diminishing human–robot interaction force, enhancing position response speed, and mitigating interaction force and position fluctuations. The experiment performed on the Franka Emika Panda robot platform, illustrates that the proposed method is effective and enhance human-robot collaboration.

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来源期刊
CiteScore
3.80
自引率
5.90%
发文量
50
期刊介绍: The International Journal of Intelligent Robotics and Applications (IJIRA) fosters the dissemination of new discoveries and novel technologies that advance developments in robotics and their broad applications. This journal provides a publication and communication platform for all robotics topics, from the theoretical fundamentals and technological advances to various applications including manufacturing, space vehicles, biomedical systems and automobiles, data-storage devices, healthcare systems, home appliances, and intelligent highways. IJIRA welcomes contributions from researchers, professionals and industrial practitioners. It publishes original, high-quality and previously unpublished research papers, brief reports, and critical reviews. Specific areas of interest include, but are not limited to:Advanced actuators and sensorsCollective and social robots Computing, communication and controlDesign, modeling and prototypingHuman and robot interactionMachine learning and intelligenceMobile robots and intelligent autonomous systemsMulti-sensor fusion and perceptionPlanning, navigation and localizationRobot intelligence, learning and linguisticsRobotic vision, recognition and reconstructionBio-mechatronics and roboticsCloud and Swarm roboticsCognitive and neuro roboticsExploration and security roboticsHealthcare, medical and assistive roboticsRobotics for intelligent manufacturingService, social and entertainment roboticsSpace and underwater robotsNovel and emerging applications
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