提出一种公平控制框架，让多机器人系统动态分配主导权并安全避障。
A Fairness-Oriented Control Framework for Safety-Critical Multi-Robot Systems: Alternative Authority Control
- 通过动态分配控制权，实现多机器人协同决策
- 在复杂环境中提升安全性与计算效率，避免碰撞
- 适合需要公平协作的自动驾驶、仓储机器人等场景
本文提出一种面向多机器人系统的公平控制框架，融合新提出的交替控制权（Alternative Authority Control, AAC）与柔性控制屏障函数（Flexible Control Barrier Function, F-CBF）。控制权指某机器人可自主规划路径，其余机器人则作为移动障碍物处理。AAC方法动态分配控制权，实现系统内公平且协调的运动。该方法显著提升了复杂环境中的计算效率、可扩展性与鲁棒性。F-CBF通过引入障碍物形状、速度和朝向信息，增强了对动态障碍物的精确避障能力。框架在多机器人仿真场景中得到验证，展示了其安全性、鲁棒性及计算高效性。
原文摘要 · Abstract (English)
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.
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