让机器人团队在线学习通信质量，实时保持连接
Integrating Online Learning and Connectivity Maintenance for Communication-Aware Multi-Robot Coordination
- 用高斯过程和控制屏障函数在线建模通信能力
- 20个机器人仿真中稳定维持通信质量与全局连通性
- 适合需要动态通信保障的多机器人协同场景
本文提出一种新型数据驱动控制策略，用于维护网络化多机器人系统的连通性。现有方法通常依赖预设的通信模型（基于相对距离判断能否通信），难以反映真实通信状况。为此，我们引入数据驱动连通性屏障证书，结合控制屏障函数（CBF）与高斯过程（GP），根据在线观测的通信性能，刻画成对机器人的可接受控制空间。该方法使机器人在运动过程中保持良好的成对通信质量（以接收信号强度衡量）。进一步提出数据驱动连通性维护（DCM）算法，融合（1）通信信号强度的在线学习，以及（2）基于双层优化的控制框架，确保真实多机器人通信图的全局连通性，并最小化偏离任务相关运动。我们提供了理论证明以验证算法性质，并通过最多20个机器人的仿真验证其有效性。
原文摘要 · Abstract (English)
This paper proposes a novel data-driven control strategy for maintaining connectivity in networked multi-robot systems. Existing approaches often rely on a pre-determined communication model specifying whether pairwise robots can communicate given their relative distance to guide the connectivity-aware control design, which may not capture real-world communication conditions. To relax that assumption, we present the concept of Data-driven Connectivity Barrier Certificates, which utilize Control Barrier Functions (CBF) and Gaussian Processes (GP) to characterize the admissible control space for pairwise robots based on communication performance observed online. This allows robots to maintain a satisfying level of pairwise communication quality (measured by the received signal strength) while in motion. Then we propose a Data-driven Connectivity Maintenance (DCM) algorithm that combines (1) online learning of the communication signal strength and (2) a bi-level optimization-based control framework for the robot team to enforce global connectivity of the realistic multi-robot communication graph and minimally deviate from their task-related motions. We provide theoretical proofs to justify the properties of our algorithm and demonstrate its effectiveness through simulations with up to 20 robots.
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