🤖 AI Summary
This study addresses the limited transparency of conventional four-channel bilateral teleoperation architectures in human-scale systems, which stems from high inertia, modeling complexity, and reliance on expensive, noise-prone force/torque sensors. To overcome these challenges, the authors propose a novel sensorless four-channel control framework that integrates inverse dynamics modeling into the four-channel architecture for the first time. Implemented on a customized WAM teleoperation platform, this approach achieves high transparency without external force sensors, substantially reducing system cost and complexity. The method outperforms existing two-channel, four-channel, and transparency-enhancing strategies in terms of position and force tracking accuracy, operator workload reduction, and maximum transmissible impedance. Its efficacy is further validated through whole-body contact tasks such as door opening.
📝 Abstract
The four-channel teleoperation architecture is a well-established framework for achieving transparency in bilateral systems. However, its performance in human-scale teleoperation is limited by high inertia, modeling challenges, and reliance on noisy and costly force/torque sensors. This paper introduces a sensorless four-channel architecture based on inverse dynamics modeling. The controller is implemented and validated on a customized WAM bilateral teleoperation setup. Experiments demonstrate that the proposed approach outperforms conventional two- and four-channel schemes as well as transparency-enhancement methods, improving position and force tracking, reducing operator effort, and increasing maximum transmittable impedance without external sensors. A door-opening case study involving sustained whole-body contact along the manipulator further demonstrates the effectiveness of the method in realistic human-scale manipulation tasks.