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

TL;DR

This work proposes a multi-agent informative path planning framework for sparse evidence discovery based on Gaussian belief mapping and dual-domain coverage that achieves lower final uncertainty in risk-aware settings and remains robust under limited communication, demonstrating its effectiveness for cooperative off-world robotic exploration.

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.

Figures (4)

• Figure 1: Validation in Lunar Environment 1. To efficiently explore the AOI in hazardous lunar terrain, three agents are deployed to operate collaboratively in parallel, and their trajectories are shown by the yellow, blue, and purple curves. From bottom to top, the figure presents the original lunar environment, the mixed Gaussian map, and the PRM planning layer. The red topological nodes and Gaussian-shaped distributions represent forbidden regions by imposing a higher traversal cost during path search.
• Figure 2: Overview of our framework. In the constructed map, our method gets two observation interest GP and risk GP, and mixes them with agents' intent to construct an augmented graph. Our neural network includes two main parts, the encoder and the decoder. After node inputting, the encoder relies on a self-attention block for noticing globe node belief and relationship, as context-aware node features. The decoder cares about the feature of the current node, neighboring node, planning state, and mask. Finally, input the value and action.
• Figure 3: Example of our method with 3 agents. (a) shows the sampling data from three agents and predicts the global map, with trajectories in red, green, and blue. (b) represents the ground truth map of information distribution. (c) The opposite of (a) is the risk prediction map. (d) displays the GP predictive mean and standard deviation of information distribution. (e) highlights the regions of interest. (f) shows the agents' trajectory intent distribution.
• Figure 4: Simulation and validation. Two lunar environments with different terrain.