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📄 Abstract
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.
Key Contributions
Introduces a novel decentralized algorithm for coverage control in unknown spatial environments using Gaussian Processes. It enables agents to autonomously determine trajectories by minimizing a local cost function that balances expected locational cost with variance-based exploration, inspired by GP-UCB. The algorithm operates fully decentralized, relying on local observations and neighbor communication, and uses a greedy selection strategy for scalable online GP updates.
Business Value
Enables efficient and autonomous operation of multi-agent systems for tasks like environmental surveying, search and rescue, or precision agriculture, reducing the need for constant human supervision and improving coverage efficiency.