多智能体编队中实现避障安全控制，保障协作时的实时安全性。
Collaborative Safety-Critical Formation Control with Obstacle Avoidance
- 基于控制屏障函数设计安全滤波器，支持加速度输入的避障控制。
- 在树状通信网络下证明线性收敛，动态与静态障碍场景均有效。
- 适合无人机、机器人集群等需高安全性的协同系统应用。
本文研究多智能体编队中的协同安全控制问题。针对通用分布式编队控制器，提出基于控制屏障函数（CBF）的安全滤波控制律，并将先前提出的协同安全框架扩展至具有加速度控制输入的避障场景。进一步将多障碍物碰撞避免集成进该框架，提出计算各智能体满足个体安全需求最大能力的方法。分析了协同安全算法的收敛速率，在每个智能体仅面对单个障碍且通信网络为树结构的特殊情况下，证明了所有智能体线性时间收敛到联合可行的安全动作。通过基于质量-弹簧运动学的编队控制器仿真验证了理论结果，展示了在已证明的简单情形、全连接系统中多个静态障碍以及动态障碍情况下的有限时间收敛性能。
原文摘要 · Abstract (English)
This work explores a collaborative method for ensuring safety in multi-agent formation control problems. We formulate a control barrier function (CBF) based safety filter control law for a generic distributed formation controller and extend our previously developed collaborative safety framework to an obstacle avoidance problem for agents with acceleration control inputs. We then incorporate multi-obstacle collision avoidance into the collaborative safety framework. This framework includes a method for computing the maximum capability of agents to satisfy their individual safety requirements. We analyze the convergence rate of our collaborative safety algorithm, and prove the linear-time convergence of cooperating agents to a jointly feasible safe action for all agents under the special case of a tree-structured communication network with a single obstacle for each agent. We illustrate the analytical results via simulation on a mass-spring kinematics-based formation controller and demonstrate the finite-time convergence of the collaborative safety algorithm in the simple proven case, the more general case of a fully-connected system with multiple static obstacles, and with dynamic obstacles.
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