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

Engineering notes

Key topics: autonomous driving, control. See the paper for implementation details and experimental results.

Chinese explanation / 中文解读

中文解读待补充：本站会优先为端到端自动驾驶、BEV感知、3D目标检测、轨迹预测、路径规划、LiDAR感知等高价值论文补充中文说明。

Original abstract

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.

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