Spatially Predictive Intent for Multi-Agent Coordination in UAV Exploration

Abstract

Distributed multi-UAV systems play an important role in applications such as search and rescue, disaster response, environmental monitoring, and autonomous reconnaissance. These tasks often require multiple UAVs to coordinate navigation and sensing so as to improve efficiency and expand useful environment coverage. However, in goal-directed collaborative exploration, it remains difficult to balance rapid target reaching with effective exploration of unknown areas, especially when redundant sensing and local spatial competition must also be considered. To address this challenge, we propose SPICE, a communication framework for goal-directed collaborative exploration under a frontier-graph action abstraction. The proposed framework improves coordination by learning more informative and interpretable communication among agents and by encouraging behaviors that reduce local overlap during navigation. Experimental results show that SPICE achieves a better balance between exploration quality and coordination efficiency than representative value-based baselines, yielding higher coverage and lower observation redundancy while maintaining competitive target-reaching performance.