A Spatially Informed Gaussian Process UCB Method for Decentralized Coverage Control

TL;DR

This paper introduces a decentralized coverage control algorithm using Gaussian Processes that balances exploration and exploitation, enabling agents to efficiently explore unknown environments with local communication.

Contribution

The paper proposes a novel decentralized GP-UCB based method for coverage control, allowing scalable online updates and local decision-making in unknown environments.

Findings

Effective in simulation for unknown spatial environments.

Operates fully decentralized with local observations and communication.

Balances exploration and exploitation via a GP-UCB inspired cost.

Abstract

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.