Outdoor flocking and formation flight with autonomous aerial robots

IROS2014

Vásárhelyi, G., Virágh, C., Somorjai, G., Tarcai, N., Szörényi, T., Nepusz, T., & Vicsek, T. (2014, September). Outdoor flocking and formation flight with autonomous aerial robots. In 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (pp. 3866-3873). doi:10.1109/IROS.2014.6943105

Background

This is our first drone swarm-related publication from the beginning of the drone era back in 2014, in which we presented the world’s first autonomous outdoor quadcopter flock of at least 10 drones. These drones were built upon the (almost) open source Mikrokopter system with our custom made high-level control board and XBee communication. Our drones were capable of performing self-organized flocking, formation flights and collective target following tasks based on local communication and pilot-free autonomous self-control in the times when any kind of “follow-me” or other autonomous functions were not even possible on single drones.

The project was one of the outcomes of the successful EU ERC COLLMOT grant (2009-2016) of Tamás Vicsek. See our original project page for historical information.

Monty Python intro remake with drones

An autonomous swarm of flying robots (full documentary)

An autonomous swarm of flying robots (shortened documentary)

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.

Funding

Media Coverage

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