arXiv:2511.02398v2 Announce Type: replace 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 t
The continuous advancements in AI and machine learning, particularly in decentralized systems and spatial intelligence, are enabling more sophisticated autonomous applications.
This research provides a foundational algorithm for improved decentralized coverage in unknown environments, critical for applications ranging from environmental monitoring to defence and logistics.
Decentralized robotic systems can now explore and exploit unknown spatial environments more efficiently and autonomously, reducing reliance on centralized control and extensive prior mapping.
- · Robotics companies
- · Logistics sector
- · Defence sector
- · Environmental monitoring
- · Systems relying solely on centralized control
Improved efficiency and adaptability of decentralized robotic fleets in complex or unmapped operational areas.
Reduced operational costs and increased scalability for autonomous systems deployed across wide spatial ranges.
Enhanced resilience and robustness of critical infrastructure and defence applications through self-organizing and adapting robotic networks.
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