Shared Voxel-Map-Based Cooperative Indoor UAV Guidance with a Multi-Agent Soft Actor-Critic Controller
Researchers have developed a framework for multiple drones to navigate indoor environments using shared voxel maps and a multi-agent reinforcement learning controller.
- Uses shared voxel-maps to fuse LiDAR data from multiple drones into a single world model.
- Employs Multi-Agent Soft Actor-Critic (MASAC) for decentralized continuous control.
- Converts complex 3D data into compact bird's-eye-view (BEV) representations for efficient processing.
- Enables consistent spatial fusion while allowing agents to act based on local ego-aligned crops.
The research introduces a novel framework designed to solve the complexities of indoor multi-UAV navigation. By utilizing a shared voxel-map world model, multiple drones can fuse their 360-degree LiDAR observations into a single, unified occupancy map. This map is then converted into a compact bird's-eye-view representation, providing each agent with a localized, ego-aligned view of its surroundings.
This approach utilizes a Multi-Agent Soft Actor-Critic (MASAC) controller, which allows for decentralized continuous control while maintaining spatial consistency across the fleet. This hybrid design, referred to as integrate-in-world and act-in-ego, ensures that drones can work together effectively without requiring a centralized, high-latency command structure.
By combining bird's-eye-view features with near-field obstacle data, the system enables drones to navigate dense, cluttered indoor spaces with higher precision and coordination than traditional individual-agent models.
Provides a new reinforcement learning architecture for multi-agent spatial coordination.
Offers a practical implementation of MASAC in complex, high-dimensional environments.
Improves the ability of drone swarms to navigate complex indoor spaces safely.
- Soft Actor-Critic (SAC)
- An off-policy actor-critic reinforcement learning algorithm that maximizes both expected reward and entropy.
- Voxel-Map
- A 3D grid representation of space where each cell (voxel) contains information about occupancy.
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