Human-Robot Co-Carrying Using Visual and Force Sensing

In this article, we propose a hybrid framework using visual and force sensing for human-robot co-carrying tasks. Visual is utilized to obtain human motion an observer designed estimating control input of human, which generates robot's desired toward human's intended motion. An adaptive impedance-based strategy proposed trajectory tracking with neural networks used compensate uncertainties in dynamics. Motion synchronization achieved approach yields stable efficient interaction behavior between robot, decreases effort avoids interference during the interaction. The validated by task simulations experiments.