通过在线调整连接权重，让机器人编队实时抗干扰更稳定。
Topological Online Learning for Displacement-based Formation Control
- 在边级动态调整机器人间连接权重，直接减少编队畸变。
- 硬件实验中编队畸变降低超31.4%，仿真误差减少1.2%至33.14%。
- 适合需要高鲁棒性的多机器人协同系统研究与工程应用。
本文提出基于位移的拓扑在线学习（TOLD）编队控制框架，实现边级实时自适应。与传统节点级控制器不同，TOLD在线更新交互拓扑权重，直接最小化编队畸变。提出两种策略：无约束权重的在线梯度流（OGF）和非负凸权重的在线指数梯度流（OExpGF）。理论分析表明，对于有向图上的单积分器代理，OExpGF保证渐近一致性，而OGF确保编队畸变有界。十二个机器人在间歇扰动下的仿真显示，结合节点级控制器后，中位累积均方畸变误差降低1.2%–33.14%。使用Crazyflie 2.0四轴飞行器的硬件实验表明，相比固定权重共识，中位编队畸变分别减少62%（OGF）和31.4%（OExpGF）。
原文摘要 · Abstract (English)
This paper addresses the problem of robust formation control by introducing Topological Online Learning for Displacement-based (TOLD) formation control, a real-time edge-level adaptation framework. Unlike conventional node-level robust controllers that regulate individual robot inputs without modifying the interaction topology, TOLD updates the interaction topology weights online to directly minimize formation distortion. Two strategies are proposed under the TOLD formation control framework: Online Gradient Flow (OGF) with unconstrained weights and Online Exponential Gradient Flow (OExpGF) with non-negative convex weights. Theoretical analysis establishes that, for single-integrator agents over directed graphs, OExpGF guarantees asymptotic consensus, while OGF ensures bounded formation distortion. Simulations with twelve robots under intermittent disturbances show 1.2%-33.14% median cumulative Root Mean Distortion Error reduction when augmenting TOLD with node-level controllers. Hardware experiments with Crazyflie 2.0 quadrotors demonstrate over 62% (OGF) and 31.4% (OExpGF) reduction in median formation distortion compared to fixed-weight consensus.
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