Bio-Inspired UAS Swarm-Keeping based on Computer Vision
Gonzalo Garcia, Azim Eskandarian · 2025
This paper employs a biologically inspired logic for trajectory generation for a swarm of autonomous aerial vehicles, using passive distance estimation from onboard visual cameras. The method is inspired by swarming birds that use the perception of neighboring birds to modify their own motion, based on passive sensory data. Based on birds' spatial proximity, the logic enables stable swarming without explicit inter-agent active distance control and specific neighbor identification. A decentralized technique is used that utilizes optimal guidance and control for trajectory tracking without centralized computations while progressing in a general direction and speed. Each agent, equipped with visual cameras, achieves a cohesive and coordinated contribution to the formation. The approach is validated through simulation using unmanned aircraft models controlled by nonlinear model predictive controllers, and by inferring distance from images between adjacent agents.