A Cooperation Control Framework Based on Admittance Control and Time-varying Passive Velocity Field Control for Human-Robot Co-carrying Tasks

TL;DR

This paper presents a novel cooperation control framework for human-robot co-carrying tasks that enhances safety, synchronization, and task performance by integrating admittance control with a time-varying passive velocity field approach.

Contribution

It introduces a new control framework combining reference generation, admittance-based conflict mitigation, and energy-aware passive velocity control for improved human-robot collaboration.

Findings

Enhanced task performance in human-robot co-carrying tasks.

Reduced human workload and conflict levels.

Theoretical analysis confirms system stability and energy regulation.

Abstract

Human-robot co-carrying tasks reveal their potential in both industrial and everyday applications by leveraging the strengths of both parties. Effective control of robots in these tasks requires managing the energy level in the closed-loop systems to prevent potential dangers while also minimizing motion errors to complete the shared tasks. The collaborative tasks pose numerous challenges due to varied human intentions in adapting to workspace characteristics, leading to human-robot conflicts. In this paper, we develop a cooperation control framework for human-robot co-carrying tasks constructed by utilizing reference generator and low-level controller to aim to achieve safe interaction and synchronized human-robot movement. Firstly, the human motion predictions are corrected in the event of prediction errors based on the conflicts measured by the interaction forces through admittance control, thereby mitigating conflict levels. Low-level controller using an energy-compensation passive velocity field control approach allows encoding the corrected motion to produce control torques for the robot. In this manner, the closed-loop robotic system is passive when the energy level exceeds the predetermined threshold, and otherwise. Furthermore, the proposed control approach ensures that the system's kinetic energy is compensated within a finite time interval. The passivity, stability, convergence rate of energy, and power flow regulation are analyzed from theoretical viewpoints. Human-in-the-loop experiments involving 18 participants have demonstrated that the proposed method significantly enhances task performance and reduces human workload, as evidenced by both objective metrics and subjective evaluations, with improvements confirmed by statistical tests (p < 0.05) relative to baseline methods.