Leader-Follower Cooperative Flocking for Multiple Pigeon-Inspired Flapping-Wing Robots

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Authors: Jiubin Wang, Tingting Meng, Qiang Fu, Jingyan Qin, Wei He
Cited by: 0
References: 10

Abstract

Motivated from pigeons flying in flocks, this paper delves into the outdoor flocking flight using pigeon-inspired flapping-wing robots (FWRs). Current research and implementation of cooperative flight for FWRs is scarce, generally exhibiting constrained operational environments, small number of individuals, and poor flight performance. This study aims to construct a stable outdoor flocking system based on our self-developed small FWR (USTB-Dove, 0.8 m wingspan). The objective is to achieve superior 3D flocking flight with multiple FWRs outdoors. To achieve this, we first improve the USTB-Dove to enhance its payload capacity and controllability. The upgraded platform, featuring a redesigned wing and tail, is capable of cruising for 30 minutes with a 65-gram payload. Additionally, within a leader-follower architecture, we design a guidance law and a velocity control model for the followers. To achieve superior leader tracking, the model is developed through physics-based modeling and parameter optimization of the actual FWR and is solved using the Newton-Raphson method. Furthermore, we develop a flocking architecture with distributed controllers and centralized monitoring. Virtual leader approach is adopted to minimize communication overhead, enabling stable 1-km operation with a system capacity of 31 FWRs. Ultimately, we conduct outdoor dual-FWR test to validate the leader-tracking capability. Then five-FWR test demonstrates system's stability and potential for scalability. The diameter of five-FWR 3D flock is controlled at approximately 10 m with an altitude stability of$35 \pm 3$m, which is, to our knowledge, the best performance achieved in published outdoor flocking flight research of FWRs.

Fields of Study & Subjects

Distributed Control Multi-Agent Systems Biomimetic flight and propulsion mechanisms Modular Robots and Swarm Intelligence

Publication details

Journal / source
IEEE Robotics and Automation Letters
Volume
11
Issue
3
Pages
2919–2926
Publication date
19 January 2026