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

2026-03-08

AI Summary

This work addresses the challenges of extraterrestrial multi-robot exploration, where sparse scientific targets, limited perception, hazardous terrain, and constrained communication hinder effective operation. Existing approaches often rely on predefined regions of interest and lack robust risk modeling. To overcome these limitations, the authors propose a multi-agent informative path planning framework that integrates Gaussian process belief mapping with dual-domain coverage. The method jointly models beliefs about scientific interest and environmental risk, incorporates trajectory intent representations to enable coordinated sequential decision-making, and embeds an explicit risk-aware mechanism to avoid unrecoverable regions. Experiments in simulated lunar environments demonstrate that the proposed approach significantly outperforms sampling- and greedy-based baselines across varying communication conditions and resource budgets, effectively reducing final uncertainty while maintaining robustness under weak connectivity.

Technology Category

• Intelligent Robots: Motion and Path Planning
• Multiagent Systems: Multiagent Planning
• Planning, Routing, and Scheduling: Planning under Uncertainty

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Search and Retrieval-Augmented AI: Agentic search
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

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.

Problem

• multi-agent exploration
• sparse evidence discovery
• off-world robotics
• informative path planning
• hazardous terrain

Innovation

• Gaussian belief mapping
• dual-domain coverage
• risk-aware path planning
• multi-agent exploration
• sparse evidence discovery