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.

翻译：我们提出了一种用于未知空间环境覆盖控制的新型分布式算法，该算法基于高斯过程（GPs）建模环境。为权衡探索与利用，每个智能体通过最小化局部代价函数自主确定其轨迹。受GP-UCB（高斯过程上置信界）采集函数启发，所提出的代价函数将期望位置代价与基于方差的探索项相结合，引导智能体前往预测密度高且模型不确定性大的区域。与先前工作相比，我们的算法以完全分布式方式运行，仅依赖局部观测及与相邻智能体的通信。特别地，智能体采用贪婪选择策略定期更新其诱导点，从而实现可扩展的在线GP更新。我们通过仿真验证了该算法的有效性。