# LSwarm: Efficient Collision Avoidance for Large Swarms with Coverage Constraints in Complex Urban Scenes

## TL;DR

LSwarm is a scalable collision avoidance algorithm for large UAV swarms in complex urban environments, balancing collision safety with coverage objectives, and demonstrating real-time performance in dense scenarios.

## Contribution

We introduce LSwarm, a novel collision avoidance method that integrates coverage constraints with ORCA-based velocity computation for large UAV swarms.

## Key findings

- Efficient collision avoidance for tens to hundreds of UAVs in urban scenes.
- Real-time computation of collision velocities in complex environments.
- Maintains coverage quality while avoiding obstacles.

## Abstract

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.