Vision-Based Formation Control for an Outdoor UAV Swarm With Hierarchical Architecture

Abstract

Formation control of a UAV swarm is challenging in outdoor GNSS-denied environments due to the difficulties in accomplishing relative positioning among the UAVs. This study proposes a vision-based formation control strategy that could be implemented in the absence of an external positioning system. The hierarchical architecture has been constructed for the UAV swarm using the modified leader-follower strategy. The leader UAV derives and broadcasts the locations of the follower UAVs, while the follower UAVs calculate their control inputs to achieve the desired swarm formation. The vision-based localization of the UAVs is accomplished using state-of-the-art deep learning algorithms like YOLOv7 and DeepSORT. The RflySim-based simulation has been conducted to verify the feasibility of the conceptualization, and the validation has been made with a real flight test using a swarm comprised of five quadrotors. Results show the robustness of the vision-based UAV positioning framework with a localization error within 0.3 m. Moreover, the formation control without the GNSS is achieved with a monocular camera and the entry-level AI platforms implemented onboard, which could be promoted to UAV swarm applications for broader scenarios.

Keywords

• Unmanned aerial vehicles
• formation control
• visual localization
• deep learning
• multi-object tracking