Collective Change Detection: Adaptivity to Dynamic Swarm Densities and Light Conditions in Robot Swarms

Wahby, Mostafa; Petzold, Julian; Eschke, Catriona; Schmickl, Thomas; Hamann, Heiko

doi:10.1162/isal_a_00233.xml

Cited by 15 publications

(13 citation statements)

References 21 publications

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“…On the one hand this spontaneous exploration may drive the system away from the consensus. On the other hand it allows regular exploration of different behaviours which may enable the group to better adapt to changing conditions or dynamic swarm densities (Mayya et al 2019;Wahby et al 2019).…”

Section: Introductionmentioning

confidence: 99%

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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Section: Introductionmentioning

confidence: 99%

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

et al. 2019

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“…Such an analysis could potentially provide additional information to improve the system design. Similarly to previous models of scalability, our model is also limited to static systems that do not vary the number of units (e.g., in robotics see Buyya et al [2009], Wahby et al [2019], Mayya et al [2019], Rausch et al [2019]) or the task (e.g., Matarić et al [2003]) at runtime. Our model, however, has potential to be extended for such cases by modifying the state transitions to include 'birth' and 'death' of units (system size variation), and by introducing time-varying parameters (dynamic tasks).…”

Section: Discussionmentioning

confidence: 99%

Scalability in Computing and Robotics

Hamann

Reina

2022

IEEE Trans. Comput.

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Efficient engineered systems require scalability. A scalable system has increasing performance with increasing system size. In an ideal situation, the increase in performance (e.g., speedup) corresponds to the number of units (e.g., processors, robots, users) that are added to the system (e.g., three times the number of processors in a computer would lead to three times faster computations). However, if multiple units work on the same task, then coordination among these units is required. This coordination can introduce overheads with an impact on system performance. The coordination costs can lead to sublinear improvement or even diminishing performance with increasing system size. However, there are also systems that implement efficient coordination and exploit collaboration of units to attain superlinear improvement. Modeling the scalability dynamics is key to understanding and engineering efficient systems. Known laws of scalability, such as Amdahl's law, Gustafson's law, and Gunther's Universal Scalability Law, are minimalistic phenomenological models that explain a rich variety of system behaviors through concise equations. While useful to gain general insights, the phenomenological nature of these models may limit the understanding of the underlying dynamics, as they are detached from first principles that could explain coordination overheads or synergies among units. Through a decentralized system approach, we propose a general model based on generic interactions between units that is able to describe, as specific cases, any general pattern of scalability included by previously reported laws. The proposed general model of scalability has the advantage of being built on first principles, or at least on a microscopic description of interaction between units, and therefore has the potential to contribute to a better understanding of system behavior and scalability. We show that this generic model can be applied to a diverse set of systems, such as parallel supercomputers, robot swarms, or wireless sensor networks, therefore creating a unified view on interdisciplinary design for scalability.

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“…While zealots are sometimes called by different names, like "committed agents" or "stubborns" or "stubborn individuals", their impact has been investigated from a biological perspective, in social physics models, as well as in robotic swarms. In the latter field, zealots have recently been introduced as a mechanism that allows the swarm to cope with changes in the environments (Prasetyo et al, 2019), a setting that is recently gaining momentum (Wahby et al, 2019).…”

Section: Related Workmentioning

confidence: 99%

Robot swarm democracy: the importance of informed individuals against zealots

et al. 2021

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In this paper we study a generalized case of best-of-n model, which considers three kind of agents: zealots, individuals who remain stubborn and do not change their opinion; informed agents, individuals that can change their opinion, are able to assess the quality of the different options; and uninformed agents, individuals that can change their opinion but are not able to assess the quality of the different opinions. We study the consensus in different regimes: we vary the quality of the options, the percentage of zealots and the percentage of informed versus uninformed agents. We also consider two decision mechanisms: the voter and majority rule. We study this problem using numerical simulations and mathematical models, and we validate our findings on physical kilobot experiments. We find that (1) if the number of zealots for the lowest quality option is not too high, the decision-making process is driven toward the highest quality option; (2) this effect can be improved increasing the number of informed agents that can counteract the effect of adverse zealots; (3) when the two options have very similar qualities, in order to keep high consensus to the best quality it is necessary to have higher proportions of informed agents.

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Collective Change Detection: Adaptivity to Dynamic Swarm Densities and Light Conditions in Robot Swarms

Cited by 15 publications

References 21 publications

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

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

Scalability in Computing and Robotics

Robot swarm democracy: the importance of informed individuals against zealots

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