Robot

Physics

Control engineering

Human–robot interaction

Motion control

Impedance control

Computer science

Motion (physics)

Astronomy

Quantum mechanics

Channel (broadcasting)

Trajectory

Mobile robot

Synchronization (alternating current)

Computer network

Computer vision

Control theory (sociology)

Visual servoing

Control (management)

Observer (physics)

Engineering

Artificial intelligence

IEEE Transactions on Industrial Electronics

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

Human-Robot Co-Carrying Using Visual and Force Sensing