Agile Formation Control of Drone Flocking Enhanced With Active Vision-Based Relative Localization
Peihan Zhang, Gang Chen, Yuzhu Li, Wei Dong
Abstract
The vision-based relative localization can provide effective feedback for the cooperation of aerial swarm and has been widely investigated in previous works. However, the limited field of view (FOV) inherently restricts its performance. To cope with this issue, we propose a novel distributed active vision-based relative localization framework and apply it to formation control in aerial swarms. Inspired by bird flocks in nature, we realize omnidirectional visual detection with a novel active vision structure and devise graph-based attention planning (GAP) to obtain the optimal observation target assignment for the swarm’s active vision. Then active visual detection results are fused with onboard measurements from Ultra-WideBand (UWB) and visual-inertial odometry (VIO) to obtain real-time relative positions, which further improve the formation control performance of the swarm. Simulations and experiments demonstrate that the proposed active vision system outperforms the fixed vision system in terms of estimation and formation accuracy.