多机器人在复杂环境中实时保持通信连接并恢复断连
Connectivity Maintenance and Recovery for Multi-Robot Motion Planning
- 基于贝塞尔曲线的实时规划，联合约束控制屏障与李雅普诺夫函数
- 在障碍密度20%下保持95.8%~100%连通性，远超对比方法
- 适用于微型无人机等高机动系统，实测8架纳米四轴验证
在多机器人应用中，通信连通性至关重要，但在障碍密集环境中兼顾连通性维护与整体通行能力仍具挑战。基于控制屏障函数的反应式控制器虽能在初始连通时维持连接，却常在复杂环境中陷入死锁。本文提出一种实时贝塞尔曲线约束运动规划算法MPC--CLF--CBF，可同步生成轨迹与控制输入，满足高阶控制屏障函数与控制李雅普诺夫函数约束。该规划器支持在复杂工作空间中实现连通性感知导航，并能从初始断连状态或临时障碍导致的分离中恢复连通性；同时提供解析连续时间导数，适用于四旋翼等高机动微分平坦系统。仿真中4~12个机器人在20%障碍密度下，连通时间达95.8%~100%，显著优于MPC--CBF的48.9%~61.3%且无碰撞发生。进一步通过8架Crazyflie纳米四轴无人机物理实验验证了有效性。
原文摘要 · Abstract (English)
Connectivity is crucial in many multi-robot applications, yet balancing connectivity maintenance and fleet traversability in obstacle-rich environments remains challenging. Reactive controllers based on control barrier functions can preserve connectivity when it is initially satisfied, but often struggle with deadlocks in cluttered environments. We propose a real-time Bézier-based constrained motion planning algorithm, namely MPC--CLF--CBF, that produces trajectories and control inputs concurrently, subject to high-order control barrier function and control Lyapunov function constraints. Our motion planner supports connectivity-aware navigation in cluttered workspaces and recovers connectivity from initially disconnected configurations and after temporary obstacle-induced separation; it also provides analytic continuous-time derivatives, facilitating its application to agile differentially flat systems such as quadrotors. In simulations with $4$--$12$ robots, it maintains $95.8$--$100\%$ graph-connected time at $20\%$ obstacle density, compared with $48.9$--$61.3\%$ for MPC--CBF, with no observed collisions. We further validate the planner in a physical experiment with $8$ Crazyflie nano-quadrotors.
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