Disturbance observer enhanced variable gain controller for robot teleoperation with motion capture using wearable armban
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Disturbance observer enhanced variable gain controller for robot teleoperation with motion capture using wearable armbands Darong Huang1 · Chenguang Yang2 · Zhaojie Ju3 · Shi-Lu Dai1 Received: 11 October 2019 / Accepted: 19 June 2020 © The Author(s) 2020
Abstract Disturbance observer (DOB) based controller performs well in estimating and compensating for perturbation when the external or internal unknown disturbance is slowly time varying. However, to some extent, robot manipulators usually work in complex environment with high-frequency disturbance. Thereby, to enhance tracking performance in a teleoperation system, only traditional DOB technique is insufficient. In this paper, for the purpose of constructing a feasible teleoperation scheme, we develop a novel controller that contains a variable gain scheme to deal with fast-time varying perturbation, whose gain is adjusted linearly according to human surface electromyographic signals collected from Myo wearable armband. In addition, for tracking the motion of operator’s arm, we derive five-joint-angle data of a moving human arm through two groups of quaternions generated from the armbands. Besides, the radial basis function neural networks and the disturbance observer-based control (DOBC) approaches are fused together into the proposed controller to compensate the unknown dynamics uncertainties of the slave robot as well as environmental perturbation. Experiments and simulations are conducted to demonstrated the effectiveness of the proposed strategy. Keywords Disturbance observer · Motion capture · Radial basis function neural networks · Teleoperation · Variable gain control
1 Introduction Robot teleoperation is a kind of advanced technology in which human operator/operators remotely control the far-end robot manipulator through computer intermediary (Sheridan 1995), and it plays an important role in healthcare, industrial
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Chenguang Yang [email protected] Darong Huang [email protected] Zhaojie Ju [email protected] Shi-Lu Dai [email protected]
1
Key Laboratory of Autonomous Systems and Networked Control, School of Automation Science and Engineering, South China University of Technology, Guangzhou 510640, China
2
Bristol Robotics Laboratory, University of the West of England, Bristol BS16 1QY, UK
3
School of Computing, University of Portsmouth, Portsmouth POI 2DJ, UK
production, rescue and aerospace. One of the most popular teleoperation methods is the master-salve scheme where the operator controls the master device directly to command the slave mobile robots and robotic manipulators (Luo et al. 2019; Yang et al. 2016; Veras et al. 2012). The technology of robot control has developed rapidly in the past decades (Liu et al. 2008; Yuan and Chen 2013), and plenty of sensors are employed in teleoperation system for capturing human motions, such as inertial measurement units (IMUs), vision sensors (e.g. Kinect) (Schwarz et al. 2012; Xu et al. 2018), haptic devices (Phantom Omni). In Yuan and Chen (2013), multiple wearable IMU sensors were used to dete
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