A Minimalistic 3D Self-Organized UAV Flocking Approach for Desert Exploration

TL;DR

This paper introduces a minimalistic 3D flocking method for UAV swarms that achieves cohesive and aligned movement without external communication or GPS, relying solely on onboard sensors and local proximity data.

Contribution

The work presents a novel, sensor-only flocking approach using Lennard-Jones potential for 3D UAV coordination in GNSS-denied environments, validated through real-world experiments.

Findings

Successful real-world UAV flocking without external signals

Effective collision avoidance and cohesion using minimal sensors

Demonstrated operation in GNSS-denied environments

Abstract

In this work, we propose a minimalistic swarm flocking approach for multirotor unmanned aerial vehicles (UAVs). Our approach allows the swarm to achieve cohesively and aligned flocking (collective motion), in a random direction, without externally provided directional information exchange (alignment control). The method relies on minimalistic sensory requirements as it uses only the relative range and bearing of swarm agents in local proximity obtained through onboard sensors on the UAV. Thus, our method is able to stabilize and control the flock of a general shape above a steep terrain without any explicit communication between swarm members. To implement proximal control in a three-dimensional manner, the Lennard-Jones potential function is used to maintain cohesiveness and avoid collisions between robots. The performance of the proposed approach was tested in real-world conditions by experiments with a team of nine UAVs. Experiments also present the usage of our approach on UAVs that are independent of external positioning systems such as the Global Navigation Satellite System (GNSS). Relying only on a relative visual localization through the ultraviolet direction and ranging (UVDAR) system, previously proposed by our group, the experiments verify that our system can be applied in GNSS-denied environments. The degree achieved of alignment and cohesiveness was evaluated using the metrics of order and steady-state value.