基于共享体素地图的多智能体软演员-评论家控制器协同室内无人机导航
Shared Voxel-Map-Based Cooperative Indoor UAV Guidance with a Multi-Agent Soft Actor-Critic Controller
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中文总结 AI 辅助
研究室内无人机协同导航问题，提出结合共享体素地图与多智能体软演员-评论家控制器的框架，多无人机融合LiDAR观测构建地图并提供给各智能体，模拟中控制器成功率达90.3%，经微调后在现实实验中实现稳定协同操作，证明该地图表示有效且可扩展。
中文摘要 AI 辅助
本文提出了一种协同室内无人机导航框架，将共享体素地图世界模型与多智能体软演员-评论家（MASAC）控制器相结合。多架无人机将360 LiDAR观测数据融合成一个共同的世界框架占用地图，转换为紧凑的鸟瞰图表示并作为自我对齐的局部作物提供给每个智能体。这种在世界中集成、在自我中行动的设计实现了一致的多无人机空间融合，同时保持分散的连续控制。该策略在集中训练、分散执行框架内结合了鸟瞰图地图特征、近场障碍物观测以及紧凑的目标和对等状态信息。在模拟中，学习到的控制器在走廊导航中成功率达到90.3%，优于Astar规划、人工势场控制器和先前的导航方法。为解决模拟到现实的残留不匹配问题，利用来自现实世界数据的离线模仿微调对模拟训练的策略进行进一步调整。在全球导航卫星系统（GNSS）受限的室内环境中的实际实验展示了两架无人机在日益具有挑战性的障碍物布局中的稳定协同操作。结果表明，共享体素地图表示为学习到的协同室内无人机导航提供了有效且可扩展的空间基础。
英文摘要
This paper presents a cooperative indoor UAV guidance framework that combines a shared voxel-map world model with a multi-agent Soft Actor-Critic (MASAC) controller. Multiple drones fuse 360 LiDAR observations into a common world-frame occupancy map, which is converted into a compact bird's-eye-view (BEV) representation and provided to each agent as an ego-aligned local crop. This integrate-in-world, act-in- ego design enables consistent multi-UAV spatial fusion whilst retaining decentralised continuous control. The policy combines BEV map features, near-field obstacle observations, and compact goal and peer-state information within a centralised-training, decentralised-execution framework. In simulation, the learned controller achieves a 90.3% success rate in corridor navigation, outperforming Astar planning, an artificial potential field controller, and a prior guidance method. To address residual sim-to-real mismatch, the simulation-trained policy is further adapted using offline imitation fine-tuning from real-world data. Real-world experiments in GNSS-denied indoor environments demonstrate stable two-UAV cooperative operation across increasingly chal- lenging obstacle layouts. The results show that shared voxel-map representations provide an effective and scalable spatial substrate for learned cooperative indoor UAV guidance.
