LSwarm: Efficient Collision Avoidance for Large Swarms with Coverage Constraints in Complex Urban Scenes
This addresses the problem of safe and efficient swarm operations for surveillance in complex urban settings, representing an incremental improvement over existing methods like ORCA.
The paper tackles collision avoidance for UAV swarms performing continuous surveillance in urban environments, presenting LSwarm, which efficiently computes velocities to avoid collisions with obstacles and other agents while maintaining coverage constraints, achieving computation times of a few milliseconds for tens to hundreds of agents in dense scenes.
In this paper, we address the problem of collision avoidance for a swarm of UAVs used for continuous surveillance of an urban environment. Our method, LSwarm, efficiently avoids collisions with static obstacles, dynamic obstacles and other agents in 3-D urban environments while considering coverage constraints. LSwarm computes collision avoiding velocities that (i) maximize the conformity of an agent to an optimal path given by a global coverage strategy and (ii) ensure sufficient resolution of the coverage data collected by each agent. Our algorithm is formulated based on ORCA (Optimal Reciprocal Collision Avoidance) and is scalable with respect to the size of the swarm. We evaluate the coverage performance of LSwarm in realistic simulations of a swarm of quadrotors in complex urban models. In practice, our approach can compute collision avoiding velocities for a swarm composed of tens to hundreds of agents in a few milliseconds on dense urban scenes consisting of tens of buildings.