Abstract
In order to achieve effective physical human–robot interaction, human dynamic characteristics needs to be considered in admittance control. This paper proposes a variable admittance control method for physical human–robot interaction based on trajectory prediction of human hand motion. By predicting the moving direction of the robot end tool under human guidance, the admittance control parameters are adjusted to reduce the interaction force. The end tool trajectory of the robot under human guidance is used for offline training of long and short-term memory neural network to generate trajectory predictors. Then the trajectory predictors are used in variable admittance control to predict the trajectory and movement direction of the robot end tool in real time. The variable admittance controller adjusts the damping matrix to reduce the damping value in the moving direction. Experiment results show that, using the constant admittance method as a benchmark, the interaction force of the proposed method is reduced by 23%, the trajectory error is reduced by 51%, and the operating jerk is reduced by at least 21%, which proves that the proposed method improves the accuracy and compliance of the operation.
Highlights
With the expansion of the application range of robots, it is increasingly common for humans and robots to collaborate closely in the same space, especially in the fields of industrial assembly and medical robots [1]
constant admittance control (CAC)-i corresponds to the training set Train-i, which represents the results of the human-robot interaction experiment under constant admittance, and trajectory prediction variable admittance control (TPVAC)-i is experiment results with predictor-i
CAC-1, CAC-2, TPVAC-1 and TPVAC-2 were completed by Tester 1, and the rest of the experiments were completed by Tester 2
Summary
With the expansion of the application range of robots, it is increasingly common for humans and robots to collaborate closely in the same space, especially in the fields of industrial assembly and medical robots [1]. Lee et al [21] characterized the dynamics of the human arm and robot and compensated the reaction forces using human hand impedance, improving the transparency of the pHRI control system He et al [22] investigated an admittance controller for interactive operations in a restricted task space and used an adaptive neural network to handle the trajectory tracking problem. The neural network method has achieved good results in solving the related problems of pHRI by predicting human behavior and introducing it into the variable admittance control Most of these researches do not consider the direction of motion, that is, the admittance is isotropic in the task space.
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