FSTaxis Algorithm: Bio-Inspired Emergent Gradient Taxis

Varughese, Joshua Cherian; Thenius, Ronald; Wotawa, Franz; Schmickl, Thomas

doi:10.7551/978-0-262-33936-0-ch055

Cited by 9 publications

(8 citation statements)

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“…The parameters used for simulation are the same as those used in [17] and [3] for FSTaxis and swarmtaxis respectively. In order to ensure fair comparison, a common communication range of 3 patches (distance unit in Netlogo) and an agent velocity of 0.5 patches per time step has been used for agents in both algorithms.…”

Section: Methodsmentioning

confidence: 99%

“…This approach also has disadvantages with respect to scalability as it would need a large number of robots for a longer trail. In the taxis approach presented in [3] and [17], the authors use a single bit ping as a signaling mechanism in their algorithms to achieve gradient taxis and source localization respectively. This approach seems to be the most suitable for underwater environments where robots need to stay cohesive and connected.…”

Section: Algorithmsmentioning

confidence: 99%

“…The swarmtaxis algorithm [3] and the FSTaxis [17] algorithm are two algorithms which fulfill all of the above criteria. They are similar to each other in some aspects which include the utilization of a single ping, purely local communication etc.…”

Section: Algorithmsmentioning

confidence: 99%

“…The Firefly and Slime mold Taxis (FSTaxis) algorithm [17] is an emergent gradient taxis algorithm inspired by the communication strategy used by slime mold and fireflies. In the FSTaxis algorithm, each agent has three communication states: "pinging", "refractory" and "inactive".…”

Section: The Fstaxis Algorithmmentioning

confidence: 99%

“…A typical run of the FSTaxis algorithm in simulation is shown in Figure 2(b). For more details on FSTaxis, please refer to the paper on the FSTaxis algorithm [17].…”

Section: The Fstaxis Algorithmmentioning

confidence: 99%

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Quantification and Analysis of the Resilience of Two Swarm Intelligent Algorithms

Varughese¹,

Thenius²,

Schmickl³

et al.

EPiC Series in Computing

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Nature showcases swarms of animals performing various complex tasks efficiently where capabilities of individuals alone in the swarm are often quite limited. Swarm intelligence is observed when agents in the swarm follow simple rules which enable the swarm to perform certain complex tasks. This decentralized approach of nature has inspired the artificial intelligence community to apply this approach to engineered systems. Such systems are said to have no single point of failure and thus tend be more resilient. The aim of this paper is to put this notion of resilience to the test and quantify the robustness of two swarm algorithms, namely "swarmtaxis" and "FSTaxis". The first simulation results of the effects of introducing an impairment in agent-to-agent interactions in these two swarm algorithms are presented in this paper. While the FSTaxis algorithm shows a much higher resilience to agent-to-agent communication failure, both the FSTaxis and swarmtaxis algorithms are found to have a non-zero tolerance towards such failures.

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Section: Methodsmentioning

confidence: 99%

Section: Algorithmsmentioning

confidence: 99%

Section: Algorithmsmentioning

confidence: 99%

Section: The Fstaxis Algorithmmentioning

confidence: 99%

“…A typical run of the FSTaxis algorithm in simulation is shown in Figure 2(b). For more details on FSTaxis, please refer to the paper on the FSTaxis algorithm [17].…”

Section: The Fstaxis Algorithmmentioning

confidence: 99%

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