citations #1

@article{coppola_survey_2020,
	title = {A {Survey} on {Swarming} {With} {Micro} {Air} {Vehicles}: {Fundamental} {Challenges} and {Constraints}},
	volume = {7},
	issn = {2296-9144},
	shorttitle = {A {Survey} on {Swarming} {With} {Micro} {Air} {Vehicles}},
	url = {https://www.frontiersin.org/article/10.3389/frobt.2020.00018/full},
	doi = {10.3389/frobt.2020.00018},
	urldate = {2021-05-13},
	journal = {Frontiers in Robotics and AI},
	author = {Coppola, Mario and McGuire, Kimberly N. and De Wagter, Christophe and de Croon, Guido C. H. E.},
	month = feb,
	year = {2020},
	pages = {18},
}

@article{vasarhelyi_optimized_2018,
	title = {Optimized flocking of autonomous drones in confined environments},
	volume = {3},
	issn = {2470-9476},
	url = {https://robotics.sciencemag.org/lookup/doi/10.1126/scirobotics.aat3536},
	doi = {10.1126/scirobotics.aat3536},
	language = {en},
	number = {20},
	urldate = {2021-06-30},
	journal = {Science Robotics},
	author = {Vásárhelyi, Gábor and Virágh, Csaba and Somorjai, Gergő and Nepusz, Tamás and Eiben, Agoston E. and Vicsek, Tamás},
	month = jul,
	year = {2018},
	pages = {eaat3536},
}

@article{innocente_self-organising_2019,
	title = {Self-organising swarms of firefighting drones: {Harnessing} the power of collective intelligence in decentralised multi-robot systems},
	volume = {34},
	issn = {18777503},
	shorttitle = {Self-organising swarms of firefighting drones},
	url = {https://linkinghub.elsevier.com/retrieve/pii/S1877750318310238},
	doi = {10.1016/j.jocs.2019.04.009},
	language = {en},
	urldate = {2021-06-30},
	journal = {Journal of Computational Science},
	author = {Innocente, Mauro S. and Grasso, Paolo},
	month = may,
	year = {2019},
	pages = {80--101},
}

@article{arul_lswarm:_2019,
	title = {{LSwarm}: {Efficient} {Collision} {Avoidance} for {Large} {Swarms} with {Coverage} {Constraints} in {Complex} {Urban} {Scenes}},
	shorttitle = {{LSwarm}},
	url = {http://arxiv.org/abs/1902.08379},
	abstract = {In this paper, we address the problem of collision avoidance for a swarm of UAVs used for continuous surveillance of an urban environment. Our method, LSwarm, efficiently avoids collisions with static obstacles, dynamic obstacles and other agents in 3-D urban environments while considering coverage constraints. LSwarm computes collision avoiding velocities that (i) maximize the conformity of an agent to an optimal path given by a global coverage strategy and (ii) ensure sufficient resolution of the coverage data collected by each agent. Our algorithm is formulated based on ORCA (Optimal Reciprocal Collision Avoidance) and is scalable with respect to the size of the swarm. We evaluate the coverage performance of LSwarm in realistic simulations of a swarm of quadrotors in complex urban models. In practice, our approach can compute collision avoiding velocities for a swarm composed of tens to hundreds of agents in a few milliseconds on dense urban scenes consisting of tens of buildings.},
	urldate = {2021-06-30},
	journal = {arXiv:1902.08379 [cs]},
	author = {Arul, Senthil Hariharan and Sathyamoorthy, Adarsh Jagan and Patel, Shivang and Otte, Michael and Xu, Huan and Lin, Ming C. and Manocha, Dinesh},
	month = may,
	year = {2019},
	note = {arXiv: 1902.08379},
	keywords = {Computer Science - Robotics},
}

@article{silic_field_2019,
	title = {Field {Deployment} of a {Plume} {Monitoring} {UAV} {Flock}},
	volume = {4},
	issn = {2377-3766, 2377-3774},
	url = {https://ieeexplore.ieee.org/document/8613865/},
	doi = {10.1109/LRA.2019.2893420},
	number = {2},
	urldate = {2021-06-30},
	journal = {IEEE Robotics and Automation Letters},
	author = {Silic, Matthew and Mohseni, Kamran},
	month = apr,
	year = {2019},
	pages = {769--775},
}