Agile Formation Control of Drone Flocking Enhanced with Active Vision-based Relative Localization
This work addresses formation control challenges for aerial swarms, but it is incremental as it builds on existing vision-based localization methods.
The paper tackled the limited field of view in vision-based relative localization for drone swarms by proposing a distributed active vision framework with graph-based attention planning, resulting in improved estimation and formation accuracy compared to fixed vision systems.
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, this letter proposes 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 devise graph-based attention planning (GAP) to improve the observation quality of the active vision in the swarm. Then active 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.