Object-Aware Impedance Control for Human–Robot Collaborative Task With Online Object Parameter Estimation

Abstract

Physical human-robot interactions (pHRIs) can improve robot autonomy and reduce physical demands on humans. In this paper, we consider a collaborative task with a considerably long object and no prior knowledge of the object’s parameters. An integrated control framework with an online object parameter estimator and a Cartesian object-aware impedance controller is proposed to realize complicated scenarios. During the transportation task, the object parameters are estimated online while a robot and human keep lifting an object. The perturbation motion is incorporated into the null space of the desired trajectory to enhance the estimator precision. An object-aware impedance controller is designed by incorporating the real-time estimation results to effectively transmit the intended human motion to the robot through the object. Experimental demonstrations of collaborative tasks, including object transportation and assembly, are implemented to show the effectiveness of our proposed method. The proposed controller was also compared to a conventional impedance controller through subjective testing and found to be more sensitive, requiring less human effort. Note to Practitioners—This research was motivated by the need to facilitate collaboration between humans and robots in handling heavy or considerable long objects, which can be challenging for a single operator. This paper proposes a physical Human-Robot Interaction (pHRI) approach that enables physical interaction between the human and the robot through the object without additional sensors such as camera or human-machine interfaces. To achieve collaborative task, the separation between the intended human motion and the object dynamics is essential. Most of real-world situations involve uncertain or unknown objects, making it challenging to assume prior knowledge of the target object’s properties. Therefore, this research introduces a real-time approach for estimating the dynamic parameters of unknown objects during collaboration, without requiring additional operational time. Consequently, the design of object-aware impedance controller can be achieved by real-time incorporation of object dynamics. Collaborative transportation and assembly task is demonstrated with whole-body controlled mobile manipulator and 1.5m long object. In future research, we will focus on enhancing the estimation precision through the use of physical informed neural network methods.