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首页> 外文期刊>Robotics and biomimetics. >Cognition-based variable admittance control for active compliance in flexible manipulation of heavy objects with a power-assist robotic system
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Cognition-based variable admittance control for active compliance in flexible manipulation of heavy objects with a power-assist robotic system

机译：基于认知的变量导纳控制，可通过动力辅助机器人系统灵活地控制重物

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Abstract In the first step, a 1-DOF power-assist robotic system (PARS) is developed for lifting lightweight objects. Dynamics for human–robot co-manipulation of objects is derived that considers human cognition (weight perception). Then, admittance control with position feedback and velocity controller is derived using weight perception-based dynamics. Human subjects lift an object with the PARS, and HRI (human–robot interaction) and system characteristics are analyzed. A comprehensive scheme is developed to evaluate the HRI and performance. HRI is expressed in terms of physical HRI (maneuverability, motion, safety, stability, naturalness) and cognitive HRI (workload, trust), and performance is expressed in terms of manipulation efficiency and precision. To follow the guidance of ISO/TS 15066, hazard analysis and risk assessment are conducted. A constrained optimization algorithm is proposed to determine the values of the control parameters that produce optimum HRI and performance with lowest risk. Results show that consideration of weight perception in dynamics and control helps achieve optimum HRI and performance for a set of hard constraints. In the second step, a weight perception-based novel variable admittance control scheme is proposed as an active compliance to the system, which enhances the physical HRI, trust, precision and efficiency by 53.05%, 46.78%, 3.84% and 4.98%, respectively, and reduces workload by 35.38% and thus helps achieve optimum HRI and performance for a set of soft constraints. The risk reduces due to the active compliance. Then, effectiveness of the optimization and control algorithms is validated using a multi-DOF PARS for manipulating heavy objects, and intuitive and natural HRI and performance for power-assisted heavy object manipulation are achieved through calibrating HRI and performance with that for manipulation of lightweight object.
机译：摘要第一步，开发了一种用于提升轻型物体的一自由度动力辅助机器人系统（PARS）。得出了人机交互对象的动力学，该动力学考虑了人类认知（体重感知）。然后，使用基于体重感知的动力学方法，导出具有位置反馈和速度控制器的导纳控制。人类受试者使用PARS提起物体，并分析了HRI（人机交互）和系统特性。开发了一个综合方案来评估HRI和性能。 HRI以物理HRI（可操纵性，运动，安全性，稳定性，自然性）和认知HRI（工作量，信任度）表示，而性能以操纵效率和精度表示。为了遵循ISO / TS 15066的指导，进行了危害分析和风险评估。提出了一种约束优化算法，以确定能够产生最佳HRI和性能且风险最低的控制参数的值。结果表明，在动力学和控制中考虑体重感知有助于针对一组严格的约束条件实现最佳的HRI和性能。第二步，提出了一种基于权重感知的新型可变导纳控制方案作为系统的主动依从性，从而将物理HRI，信任度，准确性和效率分别提高了53.05％，46.78％，3.84％和4.98％。 ，并减少了35.38％的工作量，从而有助于在一组软约束下实现最佳的HRI和性能。主动合规可降低风险。然后，使用多自由度PARS操纵重物来验证优化和控制算法的有效性，并且通过校准HRI和与轻型物体操纵的性能来获得直观自然的HRI和动力辅助重型物体操纵的性能。 。

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