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

Zhuoran Qiao; Tianxin Hu; Thien-Minh Nguyen; Shenghai Yuan

cs.ROMar 8, 2026

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

Authors: Zhuoran Qiao, Tianxin Hu, Thien-Minh Nguyen, Shenghai Yuan

Organizations: National University of Singapore, Singapore · Nanyang Technological University, Singapore · The University of Queensland, Australia

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 & tables

Fig. 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.

Fig. 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.

Fig. 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.

Method	Domain		Budget 2		Budget 3		Budget 4		Budget 5
Method	Interest	Risk	mean	std	mean	std	mean	std	mean	std
SGA + RRT (0.9,1.0)	$\checkmark$	$\times$	182.53	103.83	139.49	96.95	97.45	86.52	44.64	35.89
SGA + RRT (0.3,0.4)	$\checkmark$	$\times$	200.46	203.52	270.32	302.05	154.26	252.81	214.67	219.22
CAtNIPP + Greedy	$\checkmark$	$\times$	45.59	35.38	26.27	31.21	21.48	31.56	23.42	31.13
CAtNIPP + TI (8,5)	$\checkmark$	$\times$	48.57	33.87	25.51	21.34	26.18	18.32	14.24	7.08
Ours + TI (8,5)	$\checkmark$	$\checkmark$	37.30	16.26	21.76	12.34	19.32	10.24	10.99	6.16

TABLE I: Comparison between different domain-aware variants in terms of

\mathrm{Tr}(P_{f})

for 3 agents (10 trials on 30 instances).

Method	Budget 2		Budget 3		Budget 4
Method	mean	std	mean	std	mean	std
SGA + RRT (0.9,1.0)	32.63	1.82	21.23	1.78	15.26	1.14
CAtNIPP + Greedy	29.56	3.80	12.02	1.22	6.35	0.83
CAtNIPP + TI (8,5)	18.03	2.26	5.32	0.46	3.30	0.34
Ours + TI (8,5)	19.69	2.35	5.21	0.23	3.07	0.34

TABLE II: Ablation study in terms of final average

\mathrm{Tr}(P_{f})

for 10 agents (10 trials on 30 instances, single-domain).

Method	Communication Range	Budget 2		Budget 3		Budget 4
Method	Communication Range	mean	std	mean	std	mean	std
SGA + RRT (0.3,0.4)	0.3	110.32	63.57	64.34	61.99	39.74	32.59
CAtNIPP + Greedy		35.24	12.34	18.04	11.82	16.21	8.27
CAtNIPP + TI (8,5)		60.99	23.07	21.39	18.65	25.12	18.65
Ours + TI (8,5)		40.91	21.07	22.18	15.65	20.12	18.42
SGA + RRT (0.3,0.4)	0.6	154.02	72.61	152.36	62.54	148.87	61.05

TABLE III: Performance under limited communication ranges, in terms of

\mathrm{Tr}(P_{f})

, for 3 agents (10 trials on 30 instances).

Fig. 4: Simulation and validation. Two lunar environments with different terrain.

Multi-Robot Exploration

Multi-robot exploration coordinates robot teams to map unknown environments, find targets, and gather information efficiently and safely.

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Evangelos Psomiadis, Dipankar Maity, Panagiotis Tsiotras

D. Guggenheim School of Aerospace Engineering, Georgia Tech, Atlanta, GA, USA · Department of Electrical and Computer Engineering, UNC Charlotte, Charlotte, NC, USA

Multi-Robot Systems Robot Navigation Informative Path Planning Multi-Robot Exploration Gaussian Processes Multi-Robot Coordination+2

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Collaborative navigation of heterogeneous robots in unknown environments poses significant challenges due to sensing, communication, and computational limitations. In this work, a lead robot navigates toward a target while a mobile sensor robot (e.g., a drone) assists by transmitting information about its locally observed map under bandwidth constraints. We propose a framework that enables the sensor to jointly select its transmitted map points and navigation actions online, while also predicting unexplored regions of the environment. To this end, we present

β

-Sparse Gaussian Processes, a robust variational sparse Gaussian Process model for task-aware inducing point selection under cardinality constraints. Furthermore, we develop an action-selection strategy that balances task relevance with exploration. Simulations on Mars and Earth maps show that the framework can reduce path cost by 18% relative to no communication and decrease transmitted information by 76% compared to raw-data transmission baselines.

Abstract

Multi-Robot Exploration

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