用高斯过程指导无人车在未知区域自主覆盖，兼顾探索与利用。
A Spatially Informed Gaussian Process UCB Method for Decentralized Coverage Control
- 每个智能体基于局部观测和邻居通信，自主规划路径。
- 通过期望成本加不确定性项平衡探索与利用，提升覆盖率。
- 适合多智能体协同探测、资源受限的分布式场景。
我们提出一种新型去中心化覆盖控制算法，用于建模为高斯过程（GPs）的未知空间环境。为权衡探索与利用，各智能体通过最小化本地代价函数自主决定轨迹。受高斯过程上置信界（GP-UCB）启发，该代价函数结合了预期位置成本与基于方差的探索项，引导智能体向预测密度高且模型不确定性大的区域移动。相比以往工作，本算法完全去中心化，仅依赖局部观测与邻近智能体通信。特别地，智能体周期性采用贪心策略更新诱导点，实现可扩展的在线高斯过程更新。仿真结果验证了算法的有效性。
原文摘要 · Abstract (English)
We present a novel decentralized algorithm for coverage control in unknown spatial environments modeled by Gaussian Processes (GPs). To trade-off between exploration and exploitation, each agent autonomously determines its trajectory by minimizing a local cost function. Inspired by the GP-UCB (Upper Confidence Bound for GPs) acquisition function, the proposed cost combines the expected locational cost with a variance-based exploration term, guiding agents toward regions that are both high in predicted density and model uncertainty. Compared to previous work, our algorithm operates in a fully decentralized fashion, relying only on local observations and communication with neighboring agents. In particular, agents periodically update their inducing points using a greedy selection strategy, enabling scalable online GP updates. We demonstrate the effectiveness of our algorithm in simulation.
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