Optimized flocking of autonomous drones in confined environments

By G. Vásárhelyi, Csaba Virágh, G. Somorjai, Tamás Nepusz, Agoston E. Eiben, Tamás Vicsek. Science Robotics, 2018

480 citations recorded by the source corpus.

A flocking model that uses an evolutionary optimization framework is validated with a self-organized swarm of 30 drones. We address a fundamental issue of collective motion of aerial robots: how to ensure that large flocks of autonomous drones seamlessly navigate in confined spaces. The numerous existing flocking models are rarely tested on actual hardware because they typically neglect some crucial aspects of multirobot systems. Constrained motion and communication capabilities, delays, perturbations, or the presence of barriers should be modeled and treated explicitly because they have large effects on collective behavior during the cooperation of real agents. Handling these issues properly results in additional model complexity and a natural increase in the number of tunable parameters, which calls for appropriate optimization methods to be coupled tightly to model development. In this paper, we propose such a flocking model for real drones incorporating an evolutionary optimization framework with carefully chosen order parameters and fitness functions. We numerically demonstrated that the induced swarm behavior remained stable under realistic conditions for large flock sizes and notably for large velocities. We showed that coherent and realistic collective motion patterns persisted even around perturbing obstacles. Furthermore, we validated our model on real hardware, carrying out field experiments with a self-organized swarm of 30 drones. This is the largest of such aerial outdoor systems without central control reported to date exhibiting flocking with collective collision and object avoidance. The results confirmed the adequacy of our approach. Successfully controlling dozens of quadcopters will enable substantially more efficient task management in various contexts involving drones.

Subfields: Aerospace Engineering, Computer Networks and Communications, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/8c7006ceb8dd74a964ed31630da890090dadf057

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Aerospace Engineering

1. Zhepei Wang
2. Fei Gao
3. Chao Xu

In Computer Networks and Communications

1. Marco Dorigo
2. Vito Trianni
3. Chao Xu
4. Levent Bayindir

In Computer Vision and Pattern Recognition

1. Marco Dorigo
2. Fei Gao
3. Vito Trianni
4. Chao Xu
5. Dacheng Tao

Citations in the corpus

• Swarm Robotics: Past, Present, and Future (2021, background): “Swarm robotics is not limited to ground platforms: recent work has considered aquatic surface [38] and underwater robots [39], as well as swarms of flying drones [40], [41].”
• Swarm of micro flying robots in the wild (2022, methodology): “Such inconsistency further renders far-neighbor distance [>10 m on average reported in (29)] required for safety and thus unsuitability in cluttered environments.”