Multi-Agent Off-World Exploration for Sparse Evidence Discovery via Gaussian Belief Mapping and Dual-Domain Coverage

TL;DR

This paper introduces a multi-agent path planning framework for off-world exploration that uses Gaussian belief mapping and dual-domain coverage to improve evidence discovery, safety, and robustness in hazardous, communication-limited environments.

Contribution

It presents a novel Gaussian-process-based belief and dual-domain coverage approach for multi-agent exploration, enhancing safety and robustness over existing methods.

Findings

Outperforms sampling-based and greedy baselines in simulated lunar environments.

Achieves lower uncertainty and better safety in risk-aware exploration.

Remains effective under limited communication conditions.

Abstract

Off-world multi-robot exploration is challenged by sparse targets, limited sensing, hazardous terrain, and restricted communication. Many scientifically valuable clues are visually ambiguous and often require close-range observations, making efficient and safe informative path planning essential. Existing methods often rely on predefined areas of interest (AOIs), which may be incomplete or biased, and typically handle terrain risk only through soft penalties, which are insufficient for avoiding non-recoverable regions. To address these issues, we propose a multi-agent informative path planning framework for sparse evidence discovery based on Gaussian belief mapping and dual-domain coverage. The method maintains Gaussian-process-based interest and risk beliefs and combines them with trajectory-intent representations to support coordinated sequential decision-making among multiple agents. It further prioritizes search inside the AOI while preserving limited exploration outside it, thereby improving robustness to AOI bias. In addition, the risk-aware design helps agents balance information gain and operational safety in hazardous environments. Experimental results in simulated lunar environments show that the proposed method consistently outperforms sampling-based and greedy baselines under different budgets and communication ranges. In particular, it achieves lower final uncertainty in risk-aware settings and remains robust under limited communication, demonstrating its effectiveness for cooperative off-world robotic exploration.