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A Fairness-Oriented Control Framework for Safety-Critical Multi-Robot Systems: Alternative Authority Control

Lei Shi, Qichao Liu, Cheng Zhou, Xiong Li

TL;DR

The paper tackles fairness, efficiency, and safety in dynamic multi-robot systems by introducing an Alternative Authority Control (AAC) framework that dynamically assigns a single authority robot to plan paths while others act as moving obstacles, paired with a Flexible Control Barrier Function (F-CBF) to better handle dynamic obstacles. AAC splits decision-making and constraint generation across an authority robot and non-authority robots within a hierarchical MPC-FCBF architecture, using a Proximity Decision Function and predictive collision avoidance to allocate authority and ensure safety. The MPC-FCBF lower controller models robot dynamics with a bicycle model, uses dynamic obstacle ellipses, and optimizes path safety and performance, with parameters tuned by Bayesian optimization. Experiments demonstrate that AAC-MPC-FCBF improves computation time, robustness, and fairness over Leader-Follower approaches, and F-CBF outperforms CBF in dynamic, high-speed scenarios, indicating practical benefits for scalable safety-critical multi-robot operations.

Abstract

This paper proposes a fair control framework for multi-robot systems, which integrates the newly introduced Alternative Authority Control (AAC) and Flexible Control Barrier Function (F-CBF). Control authority refers to a single robot which can plan its trajectory while considering others as moving obstacles, meaning the other robots do not have authority to plan their own paths. The AAC method dynamically distributes the control authority, enabling fair and coordinated movement across the system. This approach significantly improves computational efficiency, scalability, and robustness in complex environments. The proposed F-CBF extends traditional CBFs by incorporating obstacle shape, velocity, and orientation. F-CBF enhances safety by accurate dynamic obstacle avoidance. The framework is validated through simulations in multi-robot scenarios, demonstrating its safety, robustness and computational efficiency.

A Fairness-Oriented Control Framework for Safety-Critical Multi-Robot Systems: Alternative Authority Control

TL;DR

The paper tackles fairness, efficiency, and safety in dynamic multi-robot systems by introducing an Alternative Authority Control (AAC) framework that dynamically assigns a single authority robot to plan paths while others act as moving obstacles, paired with a Flexible Control Barrier Function (F-CBF) to better handle dynamic obstacles. AAC splits decision-making and constraint generation across an authority robot and non-authority robots within a hierarchical MPC-FCBF architecture, using a Proximity Decision Function and predictive collision avoidance to allocate authority and ensure safety. The MPC-FCBF lower controller models robot dynamics with a bicycle model, uses dynamic obstacle ellipses, and optimizes path safety and performance, with parameters tuned by Bayesian optimization. Experiments demonstrate that AAC-MPC-FCBF improves computation time, robustness, and fairness over Leader-Follower approaches, and F-CBF outperforms CBF in dynamic, high-speed scenarios, indicating practical benefits for scalable safety-critical multi-robot operations.

Abstract

This paper proposes a fair control framework for multi-robot systems, which integrates the newly introduced Alternative Authority Control (AAC) and Flexible Control Barrier Function (F-CBF). Control authority refers to a single robot which can plan its trajectory while considering others as moving obstacles, meaning the other robots do not have authority to plan their own paths. The AAC method dynamically distributes the control authority, enabling fair and coordinated movement across the system. This approach significantly improves computational efficiency, scalability, and robustness in complex environments. The proposed F-CBF extends traditional CBFs by incorporating obstacle shape, velocity, and orientation. F-CBF enhances safety by accurate dynamic obstacle avoidance. The framework is validated through simulations in multi-robot scenarios, demonstrating its safety, robustness and computational efficiency.
Paper Structure (16 sections, 24 equations, 6 figures, 3 tables, 1 algorithm)

This paper contains 16 sections, 24 equations, 6 figures, 3 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Related Work
  3. Upper Framework: Alternative Authority Control
  4. Overview of AAC Framework
  5. Advantages
  6. Proximity Decision and Predictive Collision Avoidance
  7. Proximity Decision Function
  8. Predictive Collision Avoidance
  9. Lower Controller: MPC-FCBF
  10. Flexible Control Barrier Function
  11. Modelling of MPC-FCBF
  12. Parameter Tuning via Bayesian Optimization
  13. Experiments
  14. AAC Framework Validation and Analysis
  15. Safety Evaluation: CBF vs. F-CBF
  16. ...and 1 more sections

Figures (6)

  • Figure 1: Overview of system framework
  • Figure 2: F-CBF Illustration: By continuously monitoring the speed and position of dynamic obstacles, the size and orientation of the safety ellipse are dynamically adjusted to modify the boundaries of the safety region.
  • Figure 3: Test scenarios for swarm robots and autonomous driving
  • Figure 4: Comparison of target-reaching trajectories for wheeled robots under two frameworks
  • Figure 5: A comparison of trajectories with AAC and LFC
  • ...and 1 more figures