双臂机器人对称手工任务的自适应协调阻抗控制

IF 5.2 2区 计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS
Yang Zhang
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引用次数: 0

摘要

提出了一种自适应协调阻抗控制方案,解决了双臂机器人在执行对称双手任务时的内力调节和外力跟踪问题。该方案是在混合阻抗控制框架内开发的,这是通用阻抗控制方法的一个特殊变体。首先,利用双臂操纵动力学模型对接触力进行分解,并将其集成到相应的控制回路中进行阻抗参数的实时调整;然后,基于闭链系统的动力学模型,设计了不同的内力和外力阻抗参数自适应策略,以满足操作过程中各自的力控制需求。该方案通过对接触力误差进行实时阻抗参数调整,有效减轻未知路径和环境因素造成的轨迹偏差,提高双臂操作性能。最后,通过对双臂机器人的Matlab-Adams联合仿真验证了自适应协调控制方案的有效性,验证了其内力限制和外力跟踪的能力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Adaptive coordinated impedance control for dual-arm robot symmetric bimanual tasks
This paper proposes an adaptive coordinated impedance control scheme to address the challenges of internal force regulation and external force tracking in symmetric bimanual tasks performed by dual-arm robots. The scheme is developed within the hybrid impedance control framework, a specialized variant of the general impedance control approach. First, the contact force is decomposed using the dynamic model of dual-arm manipulation and integrated into the corresponding control loop for real-time impedance parameter adjustment. Then, based on the dynamic model of the closed-chain system, distinct impedance parameter adaptation strategies are designed for internal and external forces to meet their respective force control requirements during manipulation. By enabling real-time impedance parameter adjustments in response to contact force errors, the scheme effectively mitigates trajectory deviations caused by unknown paths and environmental factors, thereby improving dual-arm manipulation performance. Finally, the effectiveness of the adaptive coordinated control scheme is validated through a Matlab-Adams co-simulation on a dual-arm robot, demonstrating its capability in internal force limitation and external force tracking.
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来源期刊
Robotics and Autonomous Systems
Robotics and Autonomous Systems 工程技术-机器人学
CiteScore
9.00
自引率
7.00%
发文量
164
审稿时长
4.5 months
期刊介绍: Robotics and Autonomous Systems will carry articles describing fundamental developments in the field of robotics, with special emphasis on autonomous systems. An important goal of this journal is to extend the state of the art in both symbolic and sensory based robot control and learning in the context of autonomous systems. Robotics and Autonomous Systems will carry articles on the theoretical, computational and experimental aspects of autonomous systems, or modules of such systems.
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