Swarm robotics: a review from the swarm engineering perspective

Brambilla, Manuele; Ferrante, Eliseo; Birattari, Mauro; Dorigo, Marco

doi:10.1007/s11721-012-0075-2

Cited by 1,335 publications

(956 citation statements)

References 173 publications

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“…It is evident that a constant L corresponds to a fixed communication topology; when the communication topology evolves with time, a time-varying L(t) is used. This is identical to the diffusive coupling term one would find from (2). For problems involving assignment or routing, it helps to model the environment as a collection of "functional bins," together with accessibility conditions which restrict the agents' transition between the bins.…”

Section: B Models For Swarm Dynamical Systemsmentioning

confidence: 90%

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A Survey on Aerial Swarm Robotics

et al. 2018

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Abstract-The use of aerial swarms to solve real-world problems has been increasing steadily, accompanied by falling prices and improving performance of communication, sensing, and processing hardware. The commoditization of hardware has reduced unit costs, thereby lowering the barriers to entry to the field of aerial swarm robotics. A key enabling technology for swarms is the family of algorithms that allow the individual members of the swarm to communicate and allocate tasks amongst themselves, plan their trajectories, and coordinate their flight in such a way that the overall objectives of the swarm are achieved efficiently. These algorithms, often organized in a hierarchical fashion, endow the swarm with autonomy at every level, and the role of a human operator can be reduced, in principle, to interactions at a higher level without direct intervention. This technology depends on the clever and innovative application of theoretical tools from control and estimation. This paper reviews the state of the art of these theoretical tools, specifically focusing on how they have been developed for, and applied to, aerial swarms. Aerial swarms differ from swarms of ground-based vehicles in two respects: they operate in a three-dimensional (3-D) space, and the dynamics of individual vehicles adds an extra layer of complexity. We review dynamic modeling and conditions for stability and controllability that are essential in order to achieve cooperative flight and distributed sensing. The main sections of the paper focus on major results covering trajectory generation, task allocation, adversarial control, distributed sensing, monitoring, and mapping. Wherever possible, we indicate how the physics and subsystem technologies of aerial robots are brought to bear on these individual areas.

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Section: B Models For Swarm Dynamical Systemsmentioning

confidence: 90%

“…Compared to prior survey articles focused on robotic swarms [2], we emphasize swarms of aerial robots flying in a 3-D world. Other related survey papers on swarm robotics include [3], which focused on problems such as formation control, cooperative tasking, spatiotemporal planning, and consensus for generic multi-robot system.…”

Section: Introductionmentioning

confidence: 99%

A Survey on Aerial Swarm Robotics

et al. 2018

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“…Coating has been extensively studied in the area of swarm robotics, but not commonly treated as a stand-alone problem; it is instead examined as part of collective transport (e.g., [11]) or collective perception (e.g., see respective section of [12]). Some research focuses on coating objects as an independent task under the name of target surrounding or boundary coverage.…”

Section: Related Workmentioning

confidence: 99%

On the Runtime of Universal Coating for Programmable Matter

Derakhshandeh

Gmyr

Porter

et al. 2016

Lecture Notes in Computer Science

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Abstract. Imagine coating buildings and bridges with smart particles (also coined smart paint) that monitor structural integrity and sense and report on traffic and wind loads, leading to technology that could do such inspection jobs faster and cheaper and increase safety at the same time. In this paper, we study the problem of uniformly coating objects of arbitrary shape in the context of self-organizing programmable matter, i.e., programmable matter which consists of simple computational elements called particles that can establish and release bonds and can actively move in a self-organized way. Particles are anonymous, have constant-size memory, and utilize only local interactions in order to coat an object. We continue the study of our Universal Coating algorithm by focusing on its runtime analysis, showing that our algorithm terminates within a linear number of rounds with high probability. We also present a matching linear lower bound that holds with high probability. We use this lower bound to show a linear lower bound on the competitive gap between fully local coating algorithms and coating algorithms that rely on global information, which implies that our algorithm is also optimal in a competitive sense. Simulation results show that the competitive ratio of our algorithm may be better than linear in practice.

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“…Montes de Oca et al 2011;Mather and Hsieh 2012;Reina et al 2015b;Scheidler et al 2016;Valentini et al 2016). A comprehensive overview of these methods can be found in Brambilla et al (2013). PFSMs are microscopic, time-discrete, models that describe how the robot population probabilistically transitions between possible "states" (e.g.…”

Section: Introductionmentioning

confidence: 99%

The Information-Cost-Reward framework for understanding robot swarm foraging

2017

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Demand for autonomous swarms, where robots can cooperate with each other without human intervention, is set to grow rapidly in the near future. Currently, one of the main challenges in swarm robotics is understanding how the behaviour of individual robots leads to an observed emergent collective performance. In this paper, a novel approach to understanding robot swarms that perform foraging is proposed in the form of the InformationCost-Reward (ICR) framework. The framework relates the way in which robots obtain and share information (about where work needs to be done) to the swarm's ability to exploit that information in order to obtain reward efficiently in the context of a particular task and environment. The ICR framework can be applied to analyse underlying mechanisms that lead to observed swarm performance, as well as to inform hypotheses about the suitability of a particular robot control strategy for new swarm missions. Additionally, the informationcentred understanding that the framework offers paves a way towards a new swarm design methodology where general principles of collective robot behaviour guide algorithm design.

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Swarm robotics: a review from the swarm engineering perspective

Cited by 1,335 publications

References 173 publications

A Survey on Aerial Swarm Robotics

A Survey on Aerial Swarm Robotics

On the Runtime of Universal Coating for Programmable Matter

The Information-Cost-Reward framework for understanding robot swarm foraging

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