Abstract
In order to efficiently execute tasks, autonomous collective systems are required to rapidly reach accurate consensus, no matter how the group is distributed over the environment. Finding consensus in a group of agents that are in motion is a particularly great challenge, especially at larger scales and extensive environments. Nevertheless, numerous collective systems in nature reach consensus independently of scale, i.e. they are scale-free or scale-invariant. Inspired by these natural phenomena, the aim of our work is to improve consensus achievement in artificial systems by finding fundamental links between individual decision-making and scale-free collective behavior. For model validation we use physics-based simulations as well as a swarm robotic testbed.
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Rausch, I., Khaluf, Y., Simoens, P. (2019). Applying Scale-Invariant Dynamics to Improve Consensus Achievement of Agents in Motion. In: Rodríguez, S., et al. Distributed Computing and Artificial Intelligence, Special Sessions, 15th International Conference. DCAI 2018. Advances in Intelligent Systems and Computing, vol 801. Springer, Cham. https://doi.org/10.1007/978-3-319-99608-0_42
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DOI: https://doi.org/10.1007/978-3-319-99608-0_42
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