Position based free-motion data connecting by using minimum force-differential model

Igarashi, Ko; Katsura, Seiichiro

doi:10.1109/icmech.2015.7084033

Cited by 3 publications

(1 citation statement)

References 17 publications

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“…To resemble free motion for lifting the object with the PARS, we ignore a few force terms, for example, forces due to viscosity, friction, disturbances, reaction, and actuation. 30 The free motion dynamics helps the human feel the natural environment that the human experiences when lifting an object manually. Thus, the dynamics model simplifies as in equation ( 2)…”

Section: The Dynamics Modelmentioning

confidence: 99%

Calibrating intuitive and natural human–robot interaction and performance for power-assisted heavy object manipulation using cognition-based intelligent admittance control schemes

Rahman

Ikeura

2018

International Journal of Advanced Robotic Systems

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In the first step, a one degree of freedom power assist robotic system is developed for lifting lightweight objects. Dynamics for human-robot co-manipulation is derived that includes human cognition, for example, weight perception. A novel admittance control scheme is derived using the weight perception-based dynamics. Human subjects lift a small-sized, lightweight object with the power assist robotic system. Human-robot interaction and system characteristics are analyzed. A comprehensive scheme is developed to evaluate the human-robot interaction and performance, and a constrained optimization algorithm is developed to determine the optimum human-robot interaction and performance. The results show that the inclusion of weight perception in the control helps achieve optimum human-robot interaction and performance for a set of hard constraints. In the second step, the same optimization algorithm and control scheme are used for lifting a heavy object with a multidegree of freedom power assist robotic system. The results show that the human-robot interaction and performance for lifting the heavy object are not as good as that for lifting the lightweight object. Then, weight perception-based intelligent controls in the forms of model predictive control and vision-based variable admittance control are applied for lifting the heavy object. The results show that the intelligent controls enhance human-robot interaction and performance, help achieve optimum human-robot interaction and performance for a set of soft constraints, and produce similar human-robot interaction and performance as obtained for lifting the lightweight object. The human-robot interaction and performance for lifting the heavy object with power assist are treated as intuitive and natural because these are calibrated with those for lifting the lightweight object. The results also show that the variable admittance control outperforms the model predictive control. We also propose a method to adjust the variable admittance control for three degrees of freedom translational manipulation of heavy objects based on human intent recognition. The results are useful for developing controls of human friendly, high performance power assist robotic systems for heavy object manipulation in industries.

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Section: The Dynamics Modelmentioning

confidence: 99%

Calibrating intuitive and natural human–robot interaction and performance for power-assisted heavy object manipulation using cognition-based intelligent admittance control schemes

Rahman

Ikeura

2018

International Journal of Advanced Robotic Systems

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Grasp Rehabilitation of Stroke Patients Through Object Manipulation with an Intelligent Power Assist Robotic System

Rahman

2019

Advances in Intelligent Systems and Computing

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Cognition-based variable admittance control for active compliance in flexible manipulation of heavy objects with a power-assist robotic system

Rahman

Ikeura

2018

Robot. Biomim.

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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.

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Position based free-motion data connecting by using minimum force-differential model

Cited by 3 publications

References 17 publications

Calibrating intuitive and natural human–robot interaction and performance for power-assisted heavy object manipulation using cognition-based intelligent admittance control schemes

Calibrating intuitive and natural human–robot interaction and performance for power-assisted heavy object manipulation using cognition-based intelligent admittance control schemes

Grasp Rehabilitation of Stroke Patients Through Object Manipulation with an Intelligent Power Assist Robotic System

Cognition-based variable admittance control for active compliance in flexible manipulation of heavy objects with a power-assist robotic system

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