@inproceedings{10.1145/3512290.3528709,
author = {Stolfi, Daniel H. and Danoy, Gr'{e}goire},
title = {Optimising Autonomous Robot Swarm Parameters for Stable Formation Design},
year = {2022},
isbn = {9781450392372},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3512290.3528709},
doi = {10.1145/3512290.3528709},
abstract = {Autonomous robot swarm systems allow to address many inherent limitations of single robot systems, such as scalability and reliability. As a consequence, these have found their way into numerous applications including in the space and aerospace domains like swarm-based asteroid observation or counter-drone systems. However, achieving stable formations around a point of interest using different number of robots and diverse initial conditions can be challenging. In this article we propose a novel method for autonomous robots swarms self-organisation solely relying on their relative position (angle and distance). This work focuses on an evolutionary optimisation approach to calculate the parameters of the swarm, e.g. inter-robot distance, to achieve a reliable formation under different initial conditions. Experiments are conducted using realistic simulations and considering four case studies. The results observed after testing the optimal configurations on 72 unseen scenarios per case study showed the high robustness of our proposal since the desired formation was always achieved. The ability of self-organise around a point of interest maintaining a predefined fixed distance was also validated using real robots.},
booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference},
pages = {1281–1289},
numpages = {9},
keywords = {argos3 simulator, e-puck2, evolutionary algorithm, formation control, swarm robotics},
location = {Boston, Massachusetts},
series = {GECCO '22}
}