Scale-Free Features in Collective Robot Foraging

Rausch, Ilja; Khaluf, Yara; Simoens, Pieter

doi:10.3390/app9132667

Cited by 8 publications

(16 citation statements)

References 74 publications

(112 reference statements)

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“…Also how to investigate simple probabilistic models that rely on the foraging success probability in achieving an efficient foraging behavior (Pinciroli et al, 2012 ). Other studies have gone further to investigate whether the performance of swarms in the foraging tasks bears a particular characteristic distribution (e.g., a power law) for any of its time or space features (Khaluf and Dorigo, 2016 ; Rausch et al, 2019a ). Despite this intensive research effort, foraging of robot swarms in dynamic environments and the influence of different interaction models are still not well understood.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Foraging in Dynamic Environments Using Scale-Free Interaction Networks

2020

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Group interactions are widely observed in nature to optimize a set of critical collective behaviors, most notably sensing and decision making in uncertain environments. Nevertheless, these interactions are commonly modeled using local (proximity) networks, in which individuals interact within a certain spatial range. Recently, other interaction topologies have been revealed to support the emergence of higher levels of scalability and rapid information exchange. One prominent example is scale-free networks. In this study, we aim to examine the impact of scale-free communication when implemented for a swarm foraging task in dynamic environments. We model dynamic (uncertain) environments in terms of changes in food density and analyze the collective response of a simulated swarm with communication topology given by either proximity or scale-free networks. Our results suggest that scale-free networks accelerate the process of building up a rapid collective response to cope with the environment changes. However, this comes at the cost of lower coherence of the collective decision. Moreover, our findings suggest that the use of scale-free networks can improve swarm performance due to two side-effects introduced by using long-range interactions and frequent network regeneration. The former is a topological consequence, while the latter is a necessity due to robot motion. These two effects lead to reduced spatial correlations of a robot's behavior with its neighborhood and to an enhanced opinion mixing, i.e., more diversified information sampling. These insights were obtained by comparing the swarm performance in presence of scale-free networks to scenarios with alternative network topologies, and proximity networks with and without packet loss.

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Section: Introductionmentioning

confidence: 99%

Adaptive Foraging in Dynamic Environments Using Scale-Free Interaction Networks

2020

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“…In tasks that aim to achieve an agreement within the group, often referred to as collective decision-making problems, the individual integrates the received social feedback to modify its own behaviour and align it with its peers' behaviour (Castellano et al 2009;Baronchelli 2018;Bose et al 2017;Rausch et al 2019). Therefore, in agreement tasks, social feedback is substantial to attain a stable coherent behaviour within the swarm.…”

Section: Introductionmentioning

confidence: 99%

“…In collective decision-making systems, the interplay between social feedback and noise (e.g. individual exploration) has a crucial role in determining the collective coherence of the group (Khaluf et al 2017b(Khaluf et al , 2018Rausch et al 2019). While a general solution to design any adaptive decision-making system is not yet in reach, our goal is to advance the understanding of the interplay between noise and social feedback in collective systems by taking a bottom-up approach and by using particular case studies as a starting point for the investigation of underlying fundamental properties.…”

Section: Introductionmentioning

confidence: 99%

“…To put this relationship into focus, we assume the agent not to be able to cancel noise but only to adapt its social feedback. The advantages of modulating the social feedback have been previously investigated in systems of collective motion (Torney et al 2009;Shklarsh et al 2011;Khaluf et al 2018), foraging (Pagliara et al 2018;Pitonakova et al 2018;Talamali et al 2019a;Rausch et al 2019), and collective decision-making (Talamali et al 2019b).…”

Section: Introductionmentioning

confidence: 99%

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Coherent collective behaviour emerging from decentralised balancing of social feedback and noise

et al. 2019

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Decentralised systems composed of a large number of locally interacting agents often rely on coherent behaviour to execute coordinated tasks. Agents cooperate to reach a coherent collective behaviour by aligning their individual behaviour to the one of their neighbours. However, system noise, determined by factors such as individual exploration or errors, hampers and reduces collective coherence. The possibility to overcome noise and reach collective coherence is determined by the strength of social feedback, i.e. the number of communication links. On the one hand, scarce social feedback may lead to a noise-driven system and consequently incoherent behaviour within the group. On the other hand, excessively strong social feedback may require unnecessary computing by individual agents and/or may nullify the possible benefits of noise. In this study, we investigate the delicate balance between so

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“…Harwell and Gini proposed a set of quantitative metrics for the scalability of simulated swarms over 10,000 robots as a design tool by solving a large object gathering problem [65]. Rausch et al [38] investigated scale-free properties of artificial collective systems using simulated robot swarms. Many studies focused on mitigating the negative feedback-loop in foraging robot swarms caused by congestion [51,69,115].…”

Section: Scalabilitymentioning

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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Scale-Free Features in Collective Robot Foraging

Cited by 8 publications

References 74 publications

Adaptive Foraging in Dynamic Environments Using Scale-Free Interaction Networks

Adaptive Foraging in Dynamic Environments Using Scale-Free Interaction Networks

Coherent collective behaviour emerging from decentralised balancing of social feedback and noise

Swarm Foraging Review: Closing the Gap Between Proof and Practice

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