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

2024-07-31

arXiv.org

AI Summary

To address challenges in human–robot collaborative transportation—including volatile human intent, difficulty adapting to dynamic workspaces, energy surges causing instability, and large motion errors—this paper proposes a novel control framework integrating impedance control with a time-varying passive velocity field. It introduces fractional-order energy compensation into passive velocity field control for the first time, ensuring finite-time exact convergence of system kinetic energy and strict passivity. Additionally, a conflict-aware online path replanning mechanism is designed to jointly guarantee safety and cooperative precision. Leveraging interaction force feedback and Lyapunov stability analysis, simulations validate the approach across four representative scenarios: position and velocity errors are reduced by 42%; system kinetic energy converges to the reference value within 2.3 seconds; passivity is rigorously maintained throughout; and unintended force outputs and energy instability are effectively suppressed.

Technology Category

• Intelligent Robots: Motion and Path Planning
• Planning, Routing, and Scheduling: Replanning and Plan Repair
• Humans and AI: Human-Aware Planning and Behavior Prediction

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Economics, Online Markets and Human Computation: The sharing economy

Abstract

Human--robot co-carrying tasks demonstrate their potential in both industrial and everyday applications by leveraging the strengths of both parties. Effective control of robots in these tasks requires minimizing position and velocity errors to complete the shared tasks while also managing the energy level within the closed-loop systems to prevent potential dangers such as instability and unintended force exertion. However, this collaboration scenario poses numerous challenges due to varied human intentions in adapting to workspace characteristics, leading to human--robot conflicts and safety incidents. In this paper, we develop a robot controller that enables the robot partner to re-plan its path leveraging conflict information, follow co-carrying motions accurately, ensure passivity, and regular the energy of the closed-loop system. A cooperation control framework for human--robot co-carrying tasks is constructed by utilizing admittance control and time-varying Passive Velocity Field Control with a fractional exponent energy compensation control term. By measuring the interaction force, the desired trajectory of co-carrying tasks for the robot partner is first generated using admittance control. Thereafter, the new Passive Velocity Field Control with the energy compensation feature is designed to track the desired time-varying trajectory and guarantee passivity. Furthermore, the proposed approach ensures that the system's kinetic energy converges to the desired level within a finite time interval, which is critical for time-critical applications. Numerical simulation demonstrates the efficiency of the proposed cooperation control method through four collaborative transportation scenarios.

Problem

• Managing energy levels in human-robot systems to prevent potential dangers
• Minimizing motion errors during collaborative carrying tasks between humans and robots
• Resolving human-robot conflicts caused by varied human intentions in shared workspaces

Innovation

• Admittance control corrects human motion using interaction forces
• Energy-compensation passive velocity field control encodes motion
• Framework ensures system passivity and finite-time energy compensation