Multi-UAV Deployment in Obstacle-Cluttered Environments with LOS Connectivity

TL;DR

This paper presents a novel method for deploying multiple UAVs in obstacle-rich environments, ensuring LOS connectivity and collision avoidance through a minimum-edge RRT-based topology design and a distributed MPC for real-time motion coordination.

Contribution

It introduces a new constrained search algorithm for efficient UAV network topology and a distributed MPC strategy that guarantees collision-free, LOS-connected trajectories in complex environments.

Findings

The proposed method ensures collision-free, LOS-connected UAV trajectories.

Simulations demonstrate effectiveness in 3D valley environments.

Hardware experiments validate real-time adaptability.

Abstract

A reliable communication network is essential for multiple UAVs operating within obstacle-cluttered environments, where limited communication due to obstructions often occurs. A common solution is to deploy intermediate UAVs to relay information via a multi-hop network, which introduces two challenges: (i) how to design the structure of multihop networks; and (ii) how to maintain connectivity during collaborative motion. To this end, this work first proposes an efficient constrained search method based on the minimumedge RRT? algorithm, to find a spanning-tree topology that requires a less number of UAVs for the deployment task. Then, to achieve this deployment, a distributed model predictive control strategy is proposed for the online motion coordination. It explicitly incorporates not only the inter-UAV and UAVobstacle distance constraints, but also the line-of-sight (LOS) connectivity constraint. These constraints are well-known to be nonlinear and often tackled by various approximations. In contrast, this work provides a theoretical guarantee that all agent trajectories are ensured to be collision-free with a teamwise LOS connectivity at all time. Numerous simulations are performed in 3D valley-like environments, while hardware experiments validate its dynamic adaptation when the deployment position changes online.