CIMAX: collective information maximization in robotic swarms using local communication
Autor: | Ronald Thenius, Manfred Füllsack, Thomas Schmickl, Joshua Cherian Varughese, Hannes Hornischer, Franz Wotawa |
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Rok vydání: | 2020 |
Předmět: |
0209 industrial biotechnology
Computer science Distributed computing Swarm robotics Experimental and Cognitive Psychology 02 engineering and technology Maximization Group decision-making Behavioral Neuroscience 020901 industrial engineering & automation Environmental monitoring 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Bioinspiration Mobile sensor networks |
Zdroj: | Adaptive Behavior. 29:297-314 |
ISSN: | 1741-2633 1059-7123 |
Popis: | Robotic swarms and mobile sensor networks are used for environmental monitoring in various domains and areas of operation. Especially in otherwise inaccessible environments, decentralized robotic swarms can be advantageous due to their high spatial resolution of measurements and resilience to failure of individuals in the swarm. However, such robotic swarms might need to be able to compensate misplacement during deployment or adapt to dynamical changes in the environment. Reaching a collective decision in a swarm with limited communication abilities without a central entity serving as decision-maker can be a challenging task. Here, we present the CIMAX algorithm for collective decision-making for maximizing the information gathered by the swarm as a whole. Agents negotiate based on their individual sensor readings and ultimately make a decision for collectively moving in a particular direction so that the swarm as a whole increases the amount of relevant measurements and thus accessible information. We use both simulation and real robotic experiments for presenting, testing, and validating our algorithm. CIMAX is designed to be used in underwater swarm robots for troubleshooting an oxygen depletion phenomenon known as “anoxia.” |
Databáze: | OpenAIRE |
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