An Overview of Swarm Robotics: Swarm Intelligence Applied to Multi-robotics

Khaldi, Belkacem; Cherif, Foudil

doi:10.5120/ijca2015906000

Cited by 54 publications

(34 citation statements)

References 22 publications

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“…The majority of these studies were, however, performed exclusively in a simulated environment. The limited number of studies that used real robots have been conducted mostly with small (up to 15 cm) wheeled robots [33], such as the Khepera [34], the s-bot [35] and the e-puck [36]. These studies relied on abstract tasks in highly controlled environments: relatively small and enclosed arenas in laboratories.…”

Section: Evolutionary Synthesis Of Control For Srsmentioning

confidence: 99%

Evolution of Collective Behaviors for a Real Swarm of Aquatic Surface Robots

et al. 2016

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Swarm robotics is a promising approach for the coordination of large numbers of robots. While previous studies have shown that evolutionary robotics techniques can be applied to obtain robust and efficient self-organized behaviors for robot swarms, most studies have been conducted in simulation, and the few that have been conducted on real robots have been confined to laboratory environments. In this paper, we demonstrate for the first time a swarm robotics system with evolved control successfully operating in a real and uncontrolled environment. We evolve neural network-based controllers in simulation for canonical swarm robotics tasks, namely homing, dispersion, clustering, and monitoring. We then assess the performance of the controllers on a real swarm of up to ten aquatic surface robots. Our results show that the evolved controllers transfer successfully to real robots and achieve a performance similar to the performance obtained in simulation. We validate that the evolved controllers display key properties of swarm intelligence-based control, namely scalability, flexibility, and robustness on the real swarm. We conclude with a proof-of-concept experiment in which the swarm performs a complete environmental monitoring task by combining multiple evolved controllers.

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Section: Evolutionary Synthesis Of Control For Srsmentioning

confidence: 99%

Evolution of Collective Behaviors for a Real Swarm of Aquatic Surface Robots

et al. 2016

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“…In recent decades, swarm intelligence techniques in multirobotics systems are becoming among the fastest growing areas in the field of robotics [1], [2]. Swarm robotics, which has been recently attracted attention of scientists and engineers in various disciplines, has been involved in mimicking intelligent swarming of social animals like foraging, aggregation, flocking, cooperative objects transportation, and self-organized patterns formation [3]- [5].…”

Section: A the State Of The Artmentioning

confidence: 99%

A measurement-based fault detection approach applied to monitor robots swarm

Khaldi

Harrou

Sun

et al. 2017

2017 6th International Conference on Systems and Control (ICSC)

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Swarm robotics requires continuous monitoring to detect abnormal events and to sustain normal operations. Indeed, swarm robotics with one or more faulty robots leads to degradation of performances complying with the target requirements. This paper present an innovative data-driven fault detection method for monitoring robots swarm. The method combines the flexibility of principal component analysis (PCA) models and the greater sensitivity of the exponentiallyweighted moving average control chart to incipient changes. We illustrate through simulated data collected from the ARGoS simulator that a significant improvement in fault detection can be obtained by using the proposed methods as compared to the use of the conventional PCA-based methods.

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“…In fact, swarm robotics systems are strongly operating without central controllers and are expected to share several swarm features such as scalability, robustness, and flexibility. In its early age, swarm robotics systems have been usually used to replicate biological swarming tasks [3], [4] including aggregation, foraging, and pattern formation. Nowadays, much research has been directed to address the real application of such robotics systems in our daily life.…”

Section: Introductionmentioning

confidence: 99%

“…Nowadays, much research has been directed to address the real application of such robotics systems in our daily life. For instance, they can be beneficially involved in tasks like rescuing in emergency situations, collaborative search and exploration, agricultural foraging, military missions, products delivery, and warehouse managing [1], [3], [5].…”

Section: Introductionmentioning

confidence: 99%

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An Efficient Statistical Strategy to Monitor a Robot Swarm

et al. 2020

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Detecting anomalies in a robot swarm play a core role in keeping the desired performance, and meeting requirements and specifications. This letter deals with the problem of detecting anomalies in a robot swarm. In this regards, an unsupervised monitoring approach based on principal component analysis and k-nearest neighbor is proposed. The principal component analysis model is employed to generate residuals for anomaly detection. Then, the residuals are examined by computing the proposed exponentially smoothed k-nearest neighbor statistic for the purpose of anomaly detection. Here, instead of using parametric thresholds derived based on the Gaussian distribution, a nonparametric decision threshold is computed using the kernel density estimation method. This provides more flexibility to the proposed detector by relaxing assumption on the distribution underlying the data. Tests on data from ARGoS simulator show efficient performance of the proposed mechanism in monitoring a robot swarm.

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An Overview of Swarm Robotics: Swarm Intelligence Applied to Multi-robotics

Cited by 54 publications

References 22 publications

Evolution of Collective Behaviors for a Real Swarm of Aquatic Surface Robots

Evolution of Collective Behaviors for a Real Swarm of Aquatic Surface Robots

A measurement-based fault detection approach applied to monitor robots swarm

An Efficient Statistical Strategy to Monitor a Robot Swarm

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