A Relative Infrastructure-less Localization Algorithm for Decentralized and Autonomous Swarm Formation

Decentralized and autonomous control of Unmanned Aerial Vehicle (UAV) swarms is a key enabler for cooperative systems and infrastructure-less formation flights. However, UAVs often lack reliable heading angle measurements, especially in indoor scenarios, space, and GNSS-denied environments, posing an additional observability challenge on range-based relative localization. We tackle this problem by proposing a novel solution enhancing the classical tag-and-anchor trilateration. The proposed solution relies on Ultra-wideband range measurements and addresses the relative pose estimation between pairs of UAVs under relative motion. Furthermore, it does not require any explicit motion pattern or initialization procedure and leverages an approximate maximum-likelihood algorithm to recursively solve the relative localization problem with constant computational complexity. The method has been implemented and demonstrated through field experiments, where a swarm of nano-UAVs positioned themselves with respect to a leader in a nearly-static formation with an average error of 38.5 cm and a convergence time of 25 s. The achieved formation accuracy is similar to the one achieved by the state-of-the-art EKF-based leader-follower methods.