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

TL;DR

The paper tackles robust, scalable 3D UAV flocking under limited sensing by removing reliance on global positioning and centralized guidance. It introduces a magnitude-dependent proximal control based on a Lennard-Jones-inspired potential to drive a swarm of UAVs toward cohesive, random-direction motion in 3D, usable with either direct intra-swarm sensing or the UVDAR visual system for GNSS-denied operation. A two-layer control stack (MPC tracker plus SE(3) position/attitude controller) and SLERP-based heading smoothing enable smooth, feasible collective motion, validated on nine UAVs in both GNSS-enabled and GNSS-denied desert experiments. Results show high cohesion (order near 1) in GPS/communication conditions and meaningful cohesion under UVDAR with some sub-grouping due to visibility limits, highlighting practical viability and the remaining challenges of visual sensing under occlusion for real-world swarm deployment.

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.

Figures (12)

• Figure 1: Swarm of unmanned aerial vehicles in the desert using our robotic platform.
• Figure 2: UAV Model schematics.
• Figure 3: uvdar led and cameras.
• Figure 4: Representation of the bearing $\phi$ and the inclination $\theta$ in the body frame of the focal robot.
• Figure 5: A diagram of the system architecture: the Flocking Controller and the Range and Bearing Extractor in yellow are what we propose in this work. Both blocks are built upon the mrs system software (blocks in gray) Baca:2021 and supply the desired reference (position $\mathbf{r}_d$ and heading $\eta_d$) to the mrs system. In this diagram, the white blocks outline the physical design of the uav while the orange blocks outline the relative localization approaches (either using GPS and communication through Wi-Fi or using uvdar Walter:2019). Within the mrs system, we have a first layer with a mpc tracker that processes the desired reference and gives a full-state reference to the position/attitude controller. MPC tracker creates a smooth and feasible reference $\bm{\chi}$ for the reference feedback controller. The feedback Position/Attitude controller produces the desired thrust and angular velocities ($T_d$, $\bm{\omega}_d$) for the Pixhawk embedded flight controller (Attitude rate controller). The State estimator fuses data from Onboard sensors and Odometry & localization methods to create an estimate of the UAV translation and rotation ($\mathbf{x}$, $\mathbf{R}$).

Theorems & Definitions (1)

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