Flocking phase transition and threat responses in bio-inspired autonomous drone swarms

2025-12-24

AI Summary

This work addresses the challenge of simultaneously achieving multi-phase transitions and robust disturbance rejection in autonomous UAV swarms. We propose a 3D biologically inspired formation control algorithm grounded in local alignment and attraction, requiring only two neighborhood interaction gains to induce sharp phase transitions—e.g., between clustering and flocking—and to stabilize collective motion. For the first time, we empirically identify the critical region of the flocking phase transition in a real-world ten-UAV swarm. Through phase-diagram analysis, calibrated flight-dynamics modeling, and high-fidelity simulation, we demonstrate that operating near this critical regime synergistically enhances stability, flexibility, and resilience: upon intrusion, the swarm achieves sub-second collective turning and expansion, recovering high polarization within 3 seconds. Compared to non-critical operation, response speed improves by 2.1× and reorganization capability increases by 300%.

Technology Category

• Intelligent Robots: Multi-Robot Systems
• Planning, Routing, and Scheduling: Replanning and Plan Repair
• Humans and AI: Teamwork, Team formation

Application Category

• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems

Abstract

Collective motion inspired by animal groups offers powerful design principles for autonomous aerial swarms. We present a bio-inspired 3D flocking algorithm in which each drone interacts only with a minimal set of influential neighbors, relying solely on local alignment and attraction cues. By systematically tuning these two interaction gains, we map a phase diagram revealing sharp transitions between swarming and schooling, as well as a critical region where susceptibility, polarization fluctuations, and reorganization capacity peak. Outdoor experiments with a swarm of ten drones, combined with simulations using a calibrated flight-dynamics model, show that operating near this transition enhances responsiveness to external disturbances. When confronted with an intruder, the swarm performs rapid collective turns, transient expansions, and reliably recovers high alignment within seconds. These results demonstrate that minimal local-interaction rules are sufficient to generate multiple collective phases and that simple gain modulation offers an efficient mechanism to adjust stability, flexibility, and resilience in drone swarms.

Problem

• Develops a bio-inspired 3D flocking algorithm using local interactions
• Maps phase transitions between swarming and schooling by tuning interaction gains
• Demonstrates enhanced swarm responsiveness and resilience near critical transition

Innovation

• Bio-inspired 3D flocking algorithm with minimal neighbor interactions
• Phase diagram mapping via tuning alignment and attraction gains
• Operating near transition enhances swarm responsiveness and resilience