Drones, Volume 10, Issue 7
2026 July - 81 articles
Cover Story: Vision-and-language navigation for UAVs in low-altitude urban environments is challenged by visual occlusion, partial observability, dynamic scenes, and limited communication. To address these issues, this study proposes Collaborative Low-Altitude Space Navigation (Co-LASN), a dynamic Bayesian network-based framework for embodied multi-agent UAV systems. Co-LASN jointly models linguistic constraints, temporal belief transitions, environmental dynamics, and inter-agent dependencies. Each UAV updates its belief state by integrating visual observations, natural-language instructions, historical information, and compact messages from neighboring agents, while making decentralized decisions. Experiments demonstrate that this belief-aware collaboration improves navigation performance and robustness. View this paper
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Drones - ISSN 2504-446X