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Human-Centered Cooperative Control Coupling Autonomous and Haptic Shared Control via Control Barrier Function

Eito Sato, Takahiro Wada

TL;DR

The paper addresses the mismatch between autonomous control and human intent in teleoperation by introducing a time-varying control barrier function (CBF) that enables a human to define a fully autonomous control (F-AC) region while allowing haptic shared control (HSC) outside that region. The human-centered CBF extends this idea by modeling the human target as a circle with center $\bm{x}_h$ and radius $\alpha$, and by incorporating the region’s size into the barrier to smoothly switch control between F-AC and HSC while ensuring convergence to the human goal when outside the region. A pilot Unity-based teleoperation experiment with a simulated underwater vehicle shows that the proposed method improves accuracy and reduces task time in Regions requiring precision or timely human intervention, though some unintended inputs occur during transitions to manual mode. The work contributes a rigorous time-varying CBF framework and its human-centered extension, enabling safer, more interpretable, and capable human–machine collaboration in teleoperation under uncertain sensing and dynamics.

Abstract

Haptic shared control (HSC) is effective in teleoperation when full autonomy is limited by uncertainty or sensing constraints. However, autonomous control performance achieved by maximizing HSC strength is limited because the dynamics of the joystick and human arm affect the robot's behavior. We propose a cooperative framework coupling a joystick-independent autonomous controller with HSC. A control barrier function ignores joystick inputs within a safe region determined by the human operator in real-time, while HSC is engaged otherwise. A pilot experiment on simulated tasks with tele-operated underwater robot in virtual environment demonstrated improved accuracy and reduced required time over conventional HSC.

Human-Centered Cooperative Control Coupling Autonomous and Haptic Shared Control via Control Barrier Function

TL;DR

The paper addresses the mismatch between autonomous control and human intent in teleoperation by introducing a time-varying control barrier function (CBF) that enables a human to define a fully autonomous control (F-AC) region while allowing haptic shared control (HSC) outside that region. The human-centered CBF extends this idea by modeling the human target as a circle with center and radius , and by incorporating the region’s size into the barrier to smoothly switch control between F-AC and HSC while ensuring convergence to the human goal when outside the region. A pilot Unity-based teleoperation experiment with a simulated underwater vehicle shows that the proposed method improves accuracy and reduces task time in Regions requiring precision or timely human intervention, though some unintended inputs occur during transitions to manual mode. The work contributes a rigorous time-varying CBF framework and its human-centered extension, enabling safer, more interpretable, and capable human–machine collaboration in teleoperation under uncertain sensing and dynamics.

Abstract

Haptic shared control (HSC) is effective in teleoperation when full autonomy is limited by uncertainty or sensing constraints. However, autonomous control performance achieved by maximizing HSC strength is limited because the dynamics of the joystick and human arm affect the robot's behavior. We propose a cooperative framework coupling a joystick-independent autonomous controller with HSC. A control barrier function ignores joystick inputs within a safe region determined by the human operator in real-time, while HSC is engaged otherwise. A pilot experiment on simulated tasks with tele-operated underwater robot in virtual environment demonstrated improved accuracy and reduced required time over conventional HSC.

Paper Structure

This paper contains 9 sections, 17 equations, 6 figures, 1 table.

Table of Contents

  1. Introduction
  2. Controller Design
  3. Human-assist control
  4. Human-centered control barrier function approach
  5. Pilot experiment
  6. Scenario and settings
  7. Result
  8. Discussion
  9. Conclusion

Figures (6)

  • Figure 1: The overall framework of the proposed method.
  • Figure 2: Simulation environment using Unity. The operator manipulates the joystick while monitoring a display.
  • Figure 3: Path environment of the path-following task in the simulation.
  • Figure 4: Trajectories of a representative operator using the simple HSC and proposed methods.
  • Figure 5: Lateral and vertical positions of a representative operator over time using the proposed method. The red line represents the vehicle position, and the green line represents the center of the acceptable region.
  • ...and 1 more figures

Theorems & Definitions (1)

  • Definition 1