这篇论文用共享体素地图加MASAC实现了多无人机室内导航，仿真成功率90.3%，真机也能跑。比A*和人工势场都好，还解决了sim-to-real问题。
本文提出一种结合共享体素地图世界模型与多智能体Soft Actor-Critic（MASAC）控制器的室内无人机协同导航框架。多个无人机将360度LiDAR观测融合到共同的世界坐标系占据地图中，转换为紧凑的鸟瞰图（BEV）表示，每个智能体获得自我对齐的局部裁剪。在仿真中，该控制器在走廊导航中达到90.3%的成功率，优于A*规划、人工势场控制器和先前的引导方法。通过离线模仿微调，将仿真训练的策略适应真实数据，在GNSS拒止的室内环境中实现双无人机稳定协作。
Shared Voxel-Map-Based Cooperative Indoor UAV Guidance with a Multi-Agent Soft Actor-Critic Controller
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.