Swarm Intelligence and cyber-physical systems: Concepts, challenges and future trends

Schranz, Melanie; Di, Gianni A.; Schmickl, Thomas; Elmenreich, Wilfried; Arvin, Farshad; Şekercioğlu, Y. Ahmet; Sende, Micha

doi:10.1016/j.swevo.2020.100762

Cited by 117 publications

(52 citation statements)

References 172 publications

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“…Specific examples could be the exploration of the deep sea, space, or celestial bodies, environmental monitoring, smart traffic concepts, or nano medicine. These visionary applications are further detailed by Schranz et al ( 2020 ). To implement solutions in such environments, new methods, technologies, and visions are required in order to shift swarm intelligence from swarm robotic research platforms to swarm robotic products.…”

Section: Swarm Applicationsmentioning

confidence: 99%

Swarm Robotic Behaviors and Current Applications

et al. 2020

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In swarm robotics multiple robots collectively solve problems by forming advantageous structures and behaviors similar to the ones observed in natural systems, such as swarms of bees, birds, or fish. However, the step to industrial applications has not yet been made successfully. Literature is light on real-world swarm applications that apply actual swarm algorithms. Typically, only parts of swarm algorithms are used which we refer to as basic swarm behaviors. In this paper we collect and categorize these behaviors into spatial organization, navigation, decision making, and miscellaneous. This taxonomy is then applied to categorize a number of existing swarm robotic applications from research and industrial domains. Along with the classification, we give a comprehensive overview of research platforms that can be used for testing and evaluating swarm behavior, systems that are already on the market, and projects that target a specific market. Results from this survey show that swarm robotic applications are still rare today. Many industrial projects still rely on centralized control, and even though a solution with multiple robots is employed, the principal idea of swarm robotics of distributed decision making is neglected. We identified mainly following reasons: First of all, swarm behavior emerging from local interactions is hard to predict and a proof of its eligibility for applications in an industrial context is difficult to provide. Second, current communication architectures often do not match requirements for swarm communication, which often leads to a system with a centralized communication infrastructure. Finally, testing swarms for real industrial applications is an issue, since deployment in a productive environment is typically too risky and simulations of a target system may not be sufficiently accurate. In contrast, the research platforms present a means for transforming swarm robotics solutions from theory to prototype industrial systems.

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Section: Swarm Applicationsmentioning

confidence: 99%

Swarm Robotic Behaviors and Current Applications

et al. 2020

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“…Consequently, they make a huge contribution to real-world problems such as cloud computing and task scheduling [ 18 , 19 ], wireless sensor network localization and routing [ 20 , 21 ], numerous fields of medicine [ 22 , 23 , 24 ], prediction of COVID-19 cases [ 25 ], anomaly detection [ 26 ], etc. [ 27 , 28 ].…”

Section: Preliminaries and Related Workmentioning

confidence: 99%

Chaotic Harris Hawks Optimization with Quasi-Reflection-Based Learning: An Application to Enhance CNN Design

Basha

Bačanin

Vukobrat

et al. 2021

Sensors

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The research presented in this manuscript proposes a novel Harris Hawks optimization algorithm with practical application for evolving convolutional neural network architecture to classify various grades of brain tumor using magnetic resonance imaging. The proposed improved Harris Hawks optimization method, which belongs to the group of swarm intelligence metaheuristics, further improves the exploration and exploitation abilities of the basic algorithm by incorporating a chaotic population initialization and local search, along with a replacement strategy based on the quasi-reflection-based learning procedure. The proposed method was first evaluated on 10 recent CEC2019 benchmarks and the achieved results are compared with the ones generated by the basic algorithm, as well as with results of other state-of-the-art approaches that were tested under the same experimental conditions. In subsequent empirical research, the proposed method was adapted and applied for a practical challenge of convolutional neural network design. The evolved network structures were validated against two datasets that contain images of a healthy brain and brain with tumors. The first dataset comprises well-known IXI and cancer imagining archive images, while the second dataset consists of axial T1-weighted brain tumor images, as proposed in one recently published study in the Q1 journal. After performing data augmentation, the first dataset encompasses 8.000 healthy and 8.000 brain tumor images with grades I, II, III, and IV and the second dataset includes 4.908 images with Glioma, Meningioma, and Pituitary, with 1.636 images belonging to each tumor class. The swarm intelligence-driven convolutional neural network approach was evaluated and compared to other, similar methods and achieved a superior performance. The obtained accuracy was over 95% in all conducted experiments. Based on the established results, it is reasonable to conclude that the proposed approach could be used to develop networks that can assist doctors in diagnostics and help in the early detection of brain tumors.

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“…In recent years, research studies on swarm robotics have generated increasing attention from the robotics and control communities. Swarm robotics mainly focuses on coordination control mechanisms within a group of homogeneous or heterogeneous robots by following collective and decentralized decision-making approaches that are mainly inspired by nature [1]. There are many swarm behaviors such as collective motion and flocking [2][3][4][5], aggregation [6,7], foraging [8,9], exploration [10], Fig.…”

Section: Introductionmentioning

confidence: 99%

SDP-Based Robust Formation-Containment Coordination of Swarm Robotic Systems with Input Saturation

Lennox

et al. 2021

J Intell Robot Syst

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There are many potential applications of swarm robotic systems in real-world scenarios. In this paper, formation-containment controller design for single-integrator and double-integrator swarm robotic systems with input saturation is investigated. The swarm system contains two types of robots—leaders and followers. A novel control protocol and an implementation algorithm are proposed that enable the leaders to achieve the desired formation via semidefinite programming (SDP) techniques. The followers then converge into the convex hull formed by the leaders simultaneously. In contrast to conventional consensus-based formation control methods, the relative formation reference signal is not required in the real-time data transmission, which provides greater feasibility for implementation on hardware platforms. The effectiveness of the proposed formation-containment control algorithm is demonstrated with both numerical simulations and experiments using real robots that utilize the miniature mobile robot, Mona.

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Swarm Intelligence and cyber-physical systems: Concepts, challenges and future trends

Cited by 117 publications

References 172 publications

Swarm Robotic Behaviors and Current Applications

Swarm Robotic Behaviors and Current Applications

Chaotic Harris Hawks Optimization with Quasi-Reflection-Based Learning: An Application to Enhance CNN Design

SDP-Based Robust Formation-Containment Coordination of Swarm Robotic Systems with Input Saturation

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