Estimation of continuous environments by robot swarms: Correlated networks and decision-making

• URL: http://arxiv.org/abs/2302.13629v1
• Date: Mon, 27 Feb 2023 09:57:15 GMT
• Title: Estimation of continuous environments by robot swarms: Correlated networks and decision-making
• Authors: Mohsen Raoufi, Pawel Romanczuk, Heiko Hamann
• Abstract summary: Large-scale multi-robot systems need collective decision-making to establish autonomy on the swarm level. We propose a control algorithm and study it in real-world robot swarm experiments in different environments. We show that our approach is effective and achieves higher precision than a control experiment.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Collective decision-making is an essential capability of large-scale multi-robot systems to establish autonomy on the swarm level. A large portion of literature on collective decision-making in swarm robotics focuses on discrete decisions selecting from a limited number of options. Here we assign a decentralized robot system with the task of exploring an unbounded environment, finding consensus on the mean of a measurable environmental feature, and aggregating at areas where that value is measured (e.g., a contour line). A unique quality of this task is a causal loop between the robots' dynamic network topology and their decision-making. For example, the network's mean node degree influences time to convergence while the currently agreed-on mean value influences the swarm's aggregation location, hence, also the network structure as well as the precision error. We propose a control algorithm and study it in real-world robot swarm experiments in different environments. We show that our approach is effective and achieves higher precision than a control experiment. We anticipate applications, for example, in containing pollution with surface vehicles.

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