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Franz Wotawa
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Proceedings Papers
. isal2019, ALIFE 2019: The 2019 Conference on Artificial Life634-641, (July 29–August 2, 2019) 10.1162/isal_a_00232
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Mobile sensor networks and robotic swarms are being used for monitoring and exploring environments or environmental events due to the advantages offered by their distributed nature. However, coordination and self-organization of a large number of individuals is often costly in terms of energy and computation power, thus limiting the longevity of the distributed system. In this paper we present a bio-inspired algorithm enabling a robotic swarm to collectively detect anomalies in environmental parameters in a self-organized, reliable and energy efficient manner. Individuals in the swarm communicate via 1-bit signals to collectively confirm the detection of an anomaly while minimizing energy spent for communication and taking measurements. This algorithm is specifically designed for a swarm of underwater robots called “aMussels” to examine a phenomenon referred to as “anoxia” which results in oxygen depletion in the lagoon of Venice. We present the algorithm, conduct simulations and robotic experiments to examine the performance of the algorithm with respect to early detection of anoxia while minimizing energy consumption.
Proceedings Papers
. alif2016, ALIFE 2016, the Fifteenth International Conference on the Synthesis and Simulation of Living Systems330-337, (July 4–6, 2016) 10.1162/978-0-262-33936-0-ch055
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This article presents a novel bio-inspired emergent gradient taxis principle for robot swarms. The underlying communication method was inspired by slime mold and fireflies. Nature showcases a number of simple organisms which can display complex behavior in various aspects of their lives such as signaling, foraging, mating etc. Such decentralized behaviors at the organism level gives rise to an emergent intelligence such as in bees, slime mold, fireflies etc. Chemo taxis and photo taxis are known to be abilities exhibited by simple organisms without elaborate sensory and actuation capabilities. Our novel algorithm combines the underlying principles of slime mold and fireflies to achieve gradient taxis purely based on neighbor-to- neighbor communication. In this article, we present a model of the algorithm and test the algorithm in a multiagent simulation environment.