A Physical Human–Robot Interaction Framework for Trajectory Adaptation Based on Human Motion Prediction and Adaptive Impedance Control

Physical human-robot interaction (pHRI) plays an important role in robotic. In order for a human operator to be able to easily adapt to interact with a robot, a minimal interaction force in pHRI should be achieved. In this paper, a pHRI framework is proposed to allow the robot to regulate its trajectory adaptively for minimizing the interaction force with small position-tracking errors. The trajectory of the robot is first adjusted by the interaction force which is updated by the performance evaluation index. Then, the human hand motion is predicted based on the autoregressive (AR) model to further adapt the trajectory. Thirdly, an adaptive impedance control method is developed to update the stiffness in the robot impedance controller using surface electromyography (sEMG) signals for robot compliant interaction with the environment. This method allows the human operator to interact with the robot by the interaction force, the hand motion and muscle contraction. By investigating the performance of the proposed method, the interaction force is decreased and a good position tracking accuracy is achieved. Comparative experiments demonstrate the enhanced performance of the proposed method. Note to Practitioners—This paper focuses on developing a novel method that can allow the robot to compliantly interact with the human operator while simultaneously taking into account the trajectory-tracking accuracy and the interaction force in pHRI scenarios. The proposed method has a large application potential in a variety of pHRI tasks, such as human-robot collaborative transporting, curing, assembly, cutting, and so on. In addition, the proposed method can allow the human operator to physically interact with the robot in an easier and more intuitive manner, by taking advantage of human motion prediction and adaptive impedance control. Therefore, it is also potentially utilized for rehabilitation and assistive robots, and robot learning skills from human physical demonstration.

I. Introduction

Applications of robots have attracted more and more attention in recent years [1], [2], [3], [4], [5], [6]. However, in an increasing number of robotic applications, the physical interaction of a human with a robotic system is essential. Particularly for products that cannot be fully automated, the physical interaction between humans and robots is expected to improve the overall efficiency of various manufacturing tasks and processes. Physical human-robot interaction (pHRI) as a branch of robotics has penetrated every aspect of human society, such as in rehabilitation [7], industry [8] and agriculture [9]. It’s also a hot topic, especially when the focus is on transporting, slicing, cutting, polishing tasks and so on [10] and [11]. When a human operator guides a robot to perform a tooling task, he or she must apply a certain force to the robot to move it along the planned trajectory and achieve a satisfactory performance. Performances largely depend on the the robot’s ability of adapting their movements based on human intentions in pHRI tasks. Namely, the trajectory of the robot needs to be regulated to adapt to the human partner’s motion.