提升多机器人覆盖控制的结构鲁棒性，防止单个机器人失效引发系统崩溃。
On Enhancing Structural Resilience of Multirobot Coverage Control with Bearing Rigidity
- 分层框架：中心化划分区域 + 分布式模型预测控制
- 实现机器人失联后网络自恢复，保持最小刚性结构
- 适合高可靠性要求的协同任务场景
多机器人覆盖控制广泛用于高效协调机器人团队覆盖指定区域。然而，当部分机器人因故障或网络攻击导致位置偏移或丢失时，系统面临严重挑战。由于多数多机器人系统依赖通信与相对感知，单个机器人失效可能引发系统级连锁故障。本文提出一种分层覆盖框架，结合中心化维诺划分与分布式参考点跟踪模型预测控制（MPC）进行控制设计。除参考点跟踪外，分布式MPC还执行方位保持以维持多机器人系统（MRS）网络的刚性，从而增强结构鲁棒性——即在任务中检测并缓解定位误差与机器人丢失的影响。此外，证明该控制架构可在机器人丢失事件后实现系统网络恢复，并维持最小刚性结构。算法有效性通过数值仿真验证。
原文摘要 · Abstract (English)
The problem of multi-robot coverage control has been widely studied to efficiently coordinate a team of robots to cover a desired area of interest. However, this problem faces significant challenges when some robots are lost or deviate from their desired formation during the mission due to faults or cyberattacks. Since a majority of multi-robot systems (MRSs) rely on communication and relative sensing for their efficient operation, a failure in one robot could result in a cascade of failures in the entire system. In this work, we propose a hierarchical framework for area coverage, combining centralized coordination by leveraging Voronoi partitioning with decentralized reference tracking model predictive control (MPC) for control design. In addition to reference tracking, the decentralized MPC also performs bearing maintenance to enforce a rigid MRS network, thereby enhancing the structural resilience, i.e., the ability to detect and mitigate the effects of localization errors and robot loss during the mission. Furthermore, we show that the resulting control architecture guarantees the recovery of the MRS network in the event of robot loss while maintaining a minimally rigid structure. The effectiveness of the proposed algorithm is validated through numerical simulations.
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