Abstract
To solve synchronization movement problem in human-robot haptic collaboration, the robot is often required to recognize intention of the cooperator. In this paper, a method based on radial basis function neural network (RBFNN) model is presented to identify the motion intention of collaborator. Here, the human intention is defined as the desired velocity in human limb model, of which the estimation is obtained in real time based on interaction force and the contact point movement characteristics (current position and velocity of the robot) by the trained RBFNN model. To obtain training samples, adaptive impedance control method is used to control the robot during the data acquisition process, and then the data matching is executed due to the phase delay of the impedance function. The advantage of proposed intention estimation method according to the system real-time status is that the model overcomes the shortcoming of difficult estimating the human body impedance parameters. The experimental results show that this proposed method improves the synchronization of human-robot collaboration and reduces the force of the collaborator.
Highlights
Human-robot collaboration is used in many fields such as military, space technology, industry, medical treatment and healing, helping old or disabled people, and entertainment [1, 2]
The way of human-robot cooperation has the disadvantages of synchronization, excessive interaction force, and poor motion compliance
The interaction force comes from the six-dimensional force sensor installed at the end of the robot, where only one-dimensional force information in the direction of the slide motion is extracted
Summary
Human-robot collaboration is used in many fields such as military, space technology, industry, medical treatment and healing, helping old or disabled people, and entertainment [1, 2]. The way of human-robot cooperation has the disadvantages of synchronization, excessive interaction force, and poor motion compliance. To make the robot track a prescribed trajectory, impedance control is acknowledged to be a promising approach for interaction control. There is no doubt that, in different applications of human-robot shared control systems, the methods of changing assistant lever of the impedance parameters are often used. S.M.Mizanoor Rahman et al modified the power-assist control by using a novel control strategy based on the weight perception and load force features. The control modification reduced the excessive load forces applied by the subjects in each lifting scheme [4]
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