Geometric Aspects of Robot Navigation: From Individual Robots to Massive Particle Swarms

Fekete, Sándor P.

doi:10.1007/978-3-030-11072-7_21

Cited by 4 publications

(2 citation statements)

References 59 publications

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“…Many of the presented topics are still subject to ongoing work; see the recent survey article [14] for more details on context, content and technical details, as well as additional references for some of the described topics. We are confident that more progress on a wide range of related problems is imminent.…”

Section: Discussionmentioning

confidence: 99%

Coordinating Swarms of Objects at Extreme Dimensions

Fekete

2020

Lecture Notes in Computer Science

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We describe a variety of algorithmic challenges arising from coordination and reconfiguration of swarms of potentially many objects, ranging in size from minuscule particles all the way to far-away satellite swarms. Particular results include methods for coordinating the motion of vehicles in traffic in order to avoid inefficient stop-and-go congestions; using uniform global forces for controlling particle swarms; online triangulation and structured exploration; cohesive control for swarms of robots with only local communication; coordinated motion planning for efficiently reconfiguring an arrangement of robots; and constructing and reconfiguring large-scale structures by finite automata. All presented work is based on collaborations with a variety of authors, who are named in the respective sections of this overview.

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Section: Discussionmentioning

confidence: 99%

Coordinating Swarms of Objects at Extreme Dimensions

Fekete

2020

Lecture Notes in Computer Science

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“…The ant colony has long been an inspiration for agent based models and robotics, as well as computational algorithms (Rossi et al, 2018 ; Dorigo and Stützle, 2019 ). With the introduction of active inference into this space, it could be possible to bridge from these engineering fields, to more abstract areas such as information processing in networks, cybernetics, and collective graphical models (Sheldon and Dietterich, 2011 ; Baluška and Levin, 2016 ; Fekete, 2019 ). The Hymenopteran eusocial insects (ants, some bees and wasps) are highly biodiverse, have an increasing number of species with sequenced genomes, and have databases of ecological and trait data (e.g., Parr et al, 2017 ; Elsik et al, 2018 ; Friedman et al, 2020a ).…”

Section: Concluding Remarkmentioning

confidence: 99%

Active Inferants: An Active Inference Framework for Ant Colony Behavior

Friedman

Tschantz

Ramstead

et al. 2021

Front. Behav. Neurosci.

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In this paper, we introduce an active inference model of ant colony foraging behavior, and implement the model in a series of in silico experiments. Active inference is a multiscale approach to behavioral modeling that is being applied across settings in theoretical biology and ethology. The ant colony is a classic case system in the function of distributed systems in terms of stigmergic decision-making and information sharing. Here we specify and simulate a Markov decision process (MDP) model for ant colony foraging. We investigate a well-known paradigm from laboratory ant colony behavioral experiments, the alternating T-maze paradigm, to illustrate the ability of the model to recover basic colony phenomena such as trail formation after food location discovery. We conclude by outlining how the active inference ant colony foraging behavioral model can be extended and situated within a nested multiscale framework and systems approaches to biology more generally.

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