Abstract
Swarms of autonomous agents are useful in many applications due to their ability to accomplish tasks in a decentralized manner, making them more robust to failures. Due to the difficulty in running experiments with large numbers of hardware agents, researchers typically resort to simulations with simplifying assumptions. While some assumptions are tolerable, we feel that two assumptions have been overlooked: one, that agents take up physical space, and two, that a collision avoidance algorithm is available to add safety to an existing algorithm. While there do exist minimally invasive collision avoidance algorithms designed to add safety while minimizing interference in the intended behavior, we show they can still cause unexpected interference. We use an illustrative example with a double-milling behavior and show, through simulations, that the collision avoidance can still cause unexpected interference and careful parameter tuning is needed.
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Acknowledgements
This work was supported by the Department of the Navy, Office of Naval Research (ONR), under federal grants N00014-19-1-2121 and N00014-20-1-2042. The experiments were run on ARGO, a research computing cluster provided by the Office of Research Computing at George Mason University, VA. (http://orc.gmu.edu).
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Taylor, C., Siebold, A., Nowzari, C. (2020). On the Effects of Minimally Invasive Collision Avoidance on an Emergent Behavior. In: Dorigo, M., et al. Swarm Intelligence. ANTS 2020. Lecture Notes in Computer Science(), vol 12421. Springer, Cham. https://doi.org/10.1007/978-3-030-60376-2_27
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