Rapid Initialization Method of Unmanned Aerial Vehicle Swarm Based on VIO-UWB in Satellite Denial Environment

• Published in: Drones (Basel) Vol. 8; no. 7; p. 339
• Main Authors: Wang, Runmin, Deng, Zhongliang
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.07.2024
• Subjects: Accuracy; Algorithms; Collaboration; Communication; Constraints; cooperative positioning; Cramer-Rao bounds; Cramér–Rao lower bound (CRLB); Drone aircraft; Drones; Global positioning systems; GNSS-denied; GPS; Image enhancement; Localization; Location-based systems; Lower bounds; Magnetic compasses; Mapping; Methods; Movement; multidimensional scaling (MDS); Nodes; Optimization; Quadratic programming; quadratically constrained quadratic programming; Radio frequency identification; Satellite communications; Satellite imagery; Swarm intelligence; Unmanned aerial vehicles; China
• ISSN: 2504-446X; 2504-446X
• DOI: 10.3390/drones8070339

In environments where satellite signals are blocked, initializing UAV swarms quickly is a technical challenge, especially indoors or in areas with weak satellite signals, making it difficult to establish the relative position of the swarm. Two common methods for initialization are using the camera for joint SLAM initialization, which increases communication burden due to image feature point analysis, and obtaining a rough positional relationship using prior information through a device such as a magnetic compass, which lacks accuracy. In recent years, visual–inertial odometry (VIO) technology has significantly progressed, providing new solutions. With improved computing power and enhanced VIO accuracy, it is now possible to establish the relative position relationship through the movement of drones. This paper proposes a two-stage robust initialization method for swarms of more than four UAVs, suitable for larger-scale satellite denial scenarios. Firstly, the paper analyzes the Cramér–Rao lower bound (CRLB) problem and the moving configuration problem of the cluster to determine the optimal anchor node for the algorithm. Subsequently, a strategy is used to screen anchor nodes that are close to the lower bound of CRLB, and an optimization problem is constructed to solve the position relationship between anchor nodes through the relative motion and ranging relationship between UAVs. This optimization problem includes quadratic constraints as well as linear constraints and is a quadratically constrained quadratic programming problem (QCQP) with high robustness and high precision. After addressing the anchor node problem, this paper simplifies and improves a fast swarm cooperative positioning algorithm, which is faster than the traditional multidimensional scaling (MDS) algorithm. The results of theoretical simulations and actual UAV tests demonstrate that the proposed algorithm is advanced, superior, and effectively solves the UAV swarm initialization problem under the condition of a satellite signal rejection.