Voluntary Retreat for Decentralized Interference Reduction in Robot Swarms

Mayya, Siddharth; Pierpaoli, Pietro; Egerstedt, Magnus

doi:10.1109/icra.2019.8794124

Cited by 14 publications

(11 citation statements)

References 39 publications

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“…if the swarm exceeds its critical size S c of collecting workers it cannot achieve the maximum yield it could possibly achieve for a smaller number of workers involved in the resource transportation. This result highlights the importance of controlling the number of workers to maximise the global intake; a strategy implemented in a decentralised fashion by ants (Charbonneau et al 2015;Pagliara et al 2018), and recently investigated in the context of swarm robotics (Mayya et al 2019).…”

Section: Critical Swarm Size For Equal Qualities and Equal Distancesmentioning

confidence: 77%

“…We have further derived an optimality model accounting for congestion costs in foraging and examined the effect of resource distribution and colony size on the optimal distribution of foragers over forage patches. While others have previously considered the effect of colony size on recruitment strategy (Planqué et al 2010;Pagliara et al 2018;Mayya et al 2019), our analysis instead assumes the recruitment strategy, and considers the optimal distribution. Our simple heuristic agent controllers are able to approximate the optimal distribution for relatively small swarm sizes, although large swarms depart from optimality.…”

Section: Discussionmentioning

confidence: 99%

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Sophisticated collective foraging with minimalist agents: a swarm robotics test

et al. 2019

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How groups of cooperative foragers can achieve efficient and robust collective foraging is of interest both to biologists studying social insects and engineers designing swarm robotics systems. Of particular interest are distance-quality trade-offs and swarm-size-dependent foraging strategies. Here, we present a collective foraging system based on virtual pheromones, tested in simulation and in swarms of up to 200 physical robots. Our individual agent controllers are highly simplified, as they are based on binary pheromone sensors. Despite being simple, our individual controllers are able to reproduce classical foraging experiments conducted with more capable real ants that sense pheromone concentration and follow its gradient. One key feature of our controllers is a control parameter which balances the trade-off between distance selectivity and quality selectivity of individual foragers. We construct an optimal foraging theory model that accounts for distance and quality of resources, as well as overcrowding, and predicts a swarm-size-dependent strategy. We test swarms implementing our controllers against our optimality model and find that, for moderate swarm sizes, they can be parameterised to approximate the optimal foraging strategy. This study demonstrates the sufficiency of simple individual agent rules to generate sophisticated collective foraging behaviour.

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Section: Critical Swarm Size For Equal Qualities and Equal Distancesmentioning

confidence: 77%

Section: Discussionmentioning

confidence: 99%

Sophisticated collective foraging with minimalist agents: a swarm robotics test

et al. 2019

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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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“…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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Voluntary Retreat for Decentralized Interference Reduction in Robot Swarms

Cited by 14 publications

References 39 publications

Sophisticated collective foraging with minimalist agents: a swarm robotics test

Sophisticated collective foraging with minimalist agents: a swarm robotics test

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

Scalability in Computing and Robotics

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