Mean-field models in swarm robotics: a survey

Elamvazhuthi, Karthik; Berman, Spring

doi:10.1088/1748-3190/ab49a4

Cited by 81 publications

(62 citation statements)

References 133 publications

(172 reference statements)

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“…[55]) may also arise in mean-field models for robot swarms with a network communication structure (cf. [29]).…”

Section: Discussionmentioning

confidence: 99%

Network Structured Kinetic Models of Social Interactions

Burger

2021

Vietnam J. Math.

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The aim of this paper is to study the derivation of appropriate meso- and macroscopic models for interactions as appearing in social processes. There are two main characteristics the models take into account, namely a network structure of interactions, which we treat by an appropriate mesoscopic description, and a different role of interacting agents. The latter differs from interactions treated in classical statistical mechanics in the sense that the agents do not have symmetric roles, but there is rather an active and a passive agent. We will demonstrate how a certain form of kinetic equations can be obtained to describe such interactions at a mesoscopic level and moreover obtain macroscopic models from monokinetics solutions of those. The derivation naturally leads to systems of nonlocal reaction-diffusion equations (or in a suitable limit local versions thereof), which can explain spatial phase separation phenomena found to emerge from the microscopic interactions. We will highlight the approach in three examples, namely the evolution and coarsening of dialects in human language, the construction of social norms, and the spread of an epidemic.

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“…[55]) may also arise in mean-field models for robot swarms with a network communication structure (cf. [29]).…”

Section: Discussionmentioning

confidence: 99%

Network Structured Kinetic Models of Social Interactions

Burger

2021

Vietnam J. Math.

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“…In order to analyse the mode-switching population system introduced in the previous section we are going focus on fluid approximation-based methods motivated by application in mean-field control methods in swarm behaviours [15]. Note that Bortolussi in [7] presents a comprehensive set of results on fluid limits of population CTMCs exhibiting hybrid behaviour where the limiting behaviour is given in terms of piecewise deterministic Markov Processes (PDMP) [13].…”

Section: Limits For Mode-switching Population Systemsmentioning

confidence: 99%

“…The aim in this paper is to provide a rigorous treatment of such mode-switching population dynamics from the fluid approximation point of view, making them amenable to efficient ODE-based analysis techniques. A wealth of motivating examples can be drawn from the area of mean-field methods for control of swarm robotics [15] where the described information propagation dynamics have not yet been extensively studied.…”

Section: Introductionmentioning

confidence: 99%

Fluid Approximation–based Analysis for Mode-switching Population Dynamics

Piho

Hillston

2021

ACM Trans. Model. Comput. Simul.

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Fluid approximation results provide powerful methods for scalable analysis of models of population dynamics with large numbers of discrete states and have seen wide-ranging applications in modelling biological and computer-based systems and model checking. However, the applicability of these methods relies on assumptions that are not easily met in a number of modelling scenarios. This article focuses on one particular class of scenarios in which rapid information propagation in the system is considered. In particular, we study the case where changes in population dynamics are induced by information about the environment being communicated between components of the population via broadcast communication. We see how existing hybrid fluid limit results, resulting in piecewise deterministic Markov processes, can be adapted to such models. Finally, we propose heuristic constructions for extracting the mean behaviour from the resulting approximations without the need to simulate individual trajectories.

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“…Alternatively, mean-field approaches model the feedback loops that contribute to the complexity of the foraging task. They can make strong statements about the statistical distribution of robot states, but they require infinite swarm sizes and generally assume only local interactions without communication [27].…”

Section: Foundationsmentioning

confidence: 99%

Swarm Foraging Review: Closing the Gap Between Proof and Practice

et al. 2020

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Purpose of Review We review recent research on swarm robot foraging and contextualize it with foundational work. Recent work can be divided into two complementary camps: self-organizing algorithms that provide practical gains and analytical research focus on theoretical proofs. Recent Findings Encouragingly, the convergence between theory and practice is evident in analytical work on the scaling of transportation networks and in behavioral grammars that give formal insight into emergent properties of foraging. Augmented reality has enabled virtual pheromones to be used with hardware, blurring the line between physical and simulation experiments. Summary In this review we highlight bio-inspired and self-organizing approaches to swarm foraging and contrast them with approaches that can provide theoretical proofs, but which abstract away important features from foraging in real-world environments.

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Mean-field models in swarm robotics: a survey

Cited by 81 publications

References 133 publications

Network Structured Kinetic Models of Social Interactions

Network Structured Kinetic Models of Social Interactions

Fluid Approximation–based Analysis for Mode-switching Population Dynamics

Swarm Foraging Review: Closing the Gap Between Proof and Practice

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