Connectivity Preserving Decentralized UAV Swarm Navigation in Obstacle-laden Environments without Explicit Communication

TL;DR

This paper introduces a control approach for UAV swarms that maintains connectivity and avoids obstacles without explicit communication, using control barrier functions and optimization techniques, validated through simulations and real-world experiments.

Contribution

It proposes a novel CBF-based control method that preserves network connectivity without data exchange, overcoming limitations of traditional APF-based approaches.

Findings

CBF-based method reduces oscillations and constraint violations.

Simulation results show improved connectivity preservation.

Real quadrotor experiments validate practical effectiveness.

Abstract

This paper presents a novel control method for a group of UAVs in obstacle-laden environments while preserving sensing network connectivity without data transmission between the UAVs. By leveraging constraints rooted in control barrier functions (CBFs), the proposed method aims to overcome the limitations, such as oscillatory behaviors and frequent constraint violations, of the existing method based on artificial potential fields (APFs). More specifically, the proposed method first determines desired control inputs by considering CBF-based constraints rather than repulsive APFs. The desired inputs are then minimally modified by solving a numerical optimization problem with soft constraints. In addition to the optimization-based method, we present an approximate method without numerical optimization. The effectiveness of the proposed methods is evaluated by extensive simulations to compare the performance of the CBF-based methods with an APF-based approach. Experimental results using real quadrotors are also presented.