Sensorless human-intent-based power-assist control for indoor cooperative transport via interaction estimation and compliant motion generation

Abstract

This paper presents a sensorless human-intention-based control framework for a differential-drive power-assist mobile robot for indoor cooperative transportation. The proposed method estimates interaction-consistent motion cues in the motion domain using wheel-encoder measurements and motor-side actuation information, without relying on dedicated force or torque sensors. An interaction observer is first used to extract an acceleration-like interaction cue from the discrepancy between the commanded robot motion and the measured robot response. This signal is then processed by a human-intent analysis module, in which encoder-derived motion features and statistical class modeling are used to distinguish representative human interaction patterns from disturbance-related motion variations. The resulting interaction-related signal is separated into estimated human-induced and disturbance-induced acceleration components. The human-induced component is converted into a compliant velocity command through a virtual impedance model, whereas the disturbance-induced component is used to construct a disturbance-compensation term in the power-assist controller. Experimental results obtained in indoor cooperative transport scenarios suggest that the proposed framework provides effective assistive control behavior while reducing sensitivity to environmental disturbances.