Outdoor flocking and formation flight with autonomous aerial robots

TL;DR

This paper demonstrates a decentralized system enabling up to 10 autonomous outdoor quadcopters to perform stable flocking, formation flight, and target tracking using local communication and bio-inspired control models.

Contribution

It introduces the first decentralized outdoor multi-copter flock with autonomous navigation and collective behaviors, scalable to larger groups, using on-board computation and local GPS-based communication.

Findings

Successful autonomous outdoor flocking with 10 drones.

Stable formation flight and target tracking achieved.

Decentralized control scalable for larger groups.

Abstract

We present the first decentralized multi-copter flock that performs stable autonomous outdoor flight with up to 10 flying agents. By decentralized and autonomous we mean that all members navigate themselves based on the dynamic information received from other robots in the vicinity. We do not use central data processing or control; instead, all the necessary computations are carried out by miniature on-board computers. The only global information the system exploits is from GPS receivers, while the units use wireless modules to share this positional information with other flock members locally. Collective behavior is based on a decentralized control framework with bio-inspiration from statistical physical modelling of animal swarms. In addition, the model is optimized for stable group flight even in a noisy, windy, delayed and error-prone environment. Using this framework we successfully implemented several fundamental collective flight tasks with up to 10 units: i) we achieved self-propelled flocking in a bounded area with self-organized object avoidance capabilities and ii) performed collective target tracking with stable formation flights (grid, rotating ring, straight line). With realistic numerical simulations we demonstrated that the local broadcast-type communication and the decentralized autonomous control method allows for the scalability of the model for much larger flocks.