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.
Cheng-Lin Tang, Lei Liu, Xudong Lu et al.· Fall Joint Computer Conferen...· 0 citations
Unmanned aerial vehicles (UAVs) offer several advantages, including high mobility, flexible deployment, low cost, and strong adaptability to complex environments, making them highly promising for applications such as disaster search and rescue, environmental monitoring, inspection, and reconnaissance. For target exploration tasks in unknown environments, multiUAV systems can expand the search area, improve exploration efficiency, and enhance the robustness of task execution through cooperation, which makes this problem of significant research interest. However, such tasks still face several challenges, including partial observability of environmental information, complex cooperative decision-making, and difficulties in credit assignment among multiple UAVs. Reinforcement learning is capable of learning decision-making policies autonomously through interaction with the environment, providing a new perspective for solving cooperative exploration problems in complex environments. To address these issues, we propose a cooperative decision-making method for multi-UAV target exploration. By incorporating target-related information, the proposed method enhances the cooperative exploration capability of UAVs in unknown environments, while a tailored reward design is adopted to improve the coordination efficiency of multiple UAVs. Experimental results show that the proposed method exhibits strong adaptability to different team sizes and sensor configurations, learns effective cooperative behaviors, and outperforms classical exploration methods across multiple performance metrics, thereby demonstrating its effectiveness in multi-UAV target exploration tasks.
Batuo Zhang, Lei Liu, Zhongmin Yan et al.· Fall Joint Computer Conferen...· 0 citations
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