Adaptive data-driven error detection in swarm robotics with statistical classifiers

Lau, Hui Keng; Bate, Iain; Cairns, Paul; Timmis, Jon

doi:10.1016/j.robot.2011.08.008

Cited by 24 publications

(27 citation statements)

References 9 publications

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“…By contrast, our proposed fault-detection model is distributed in design and consequently scalable as demonstrated with up to a fivefold increase in swarm size. Furthermore, in contrast to other exogenous and distributed fault detection models, our approach is not limited to the detection of specific faults involving complete robot failure [30], and does not require detailed task-performance metrics known apriori [16,32].…”

Section: Discussionmentioning

confidence: 99%

To err is robotic, to tolerate immunological: fault detection in multirobot systems

Tarapore¹,

Lima²,

Carneiro³

et al. 2015

Bioinspir. Biomim.

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Abstract. Fault detection and fault tolerance represent two of the most important and largely unsolved issues in the field of multirobot systems. Efficient, long-term operation requires an accurate, timely detection, and accommodation of abnormally behaving robots. Most existing approaches to fault-tolerance prescribe a characterisation of normal robot behaviours, and train a model to recognise these behaviours. Behaviours unrecognised by the model are consequently labelled abnormal or faulty. Multirobot systems employing these models do not transition well to scenarios involving temporal variations in behaviour (e.g., online learning of new behaviours, or in response to environment perturbations). The vertebrate immune system is a complex distributed system capable of learning to tolerate the organism's tissues even when they change during puberty or metamorphosis, and to mount specific responses to invading pathogens, all without the need of a genetically hardwired characterisation of normality. We present a generic abnormality detection approach based on a model of the adaptive immune system, and evaluate the approach in a swarm of robots. Our results reveal the robust detection of abnormal robots simulating common electro-mechanical and software faults, irrespective of temporal changes in swarm behaviour. Abnormality detection Fault detection in multirobot system2 is shown to be scalable in terms of the number of robots in the swarm, and in terms of the size of the behaviour classification space.

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Section: Discussionmentioning

confidence: 99%

To err is robotic, to tolerate immunological: fault detection in multirobot systems

Tarapore¹,

Lima²,

Carneiro³

et al. 2015

Bioinspir. Biomim.

View full text Add to dashboard Cite

show abstract

“…On the other hand, exogenous fault detection techniques were developed to inspect several robots simultaneously [15]. In other words, a robot could detect errors that arise in another robot's components by taking into consideration the available information of its neighborhood in the swarm [15]. Owens et al [16], and Jakimovski et al [17] proposed an AIS-based fault detection algorithm inspired by the T-Cell…”

Section: The State Of the Artmentioning

confidence: 99%

Monitoring a robot swarm using a data-driven fault detection approach

Khaldi¹,

Harrou²,

Cherif³

et al. 2017

Robotics and Autonomous Systems

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Using swarm robotics system, with one or more faulty robots, to accomplish specific tasks may lead to degradation in performances complying with the target requirements. In such circumstances, robot swarms require continuous monitoring to detect abnormal events and to sustain normal operations. In this paper, an innovative exogenous fault detection method for monitoring robots swarm is presented. The method merges the flexibility of principal component analysis (PCA) models and the greater sensitivity of the exponentiallyweighted moving average (EWMA) and cumulative sum (CUSUM) control charts to insidious changes. The method is tested and evaluated on a swarm of simulated foot-bot robots performing a circle formation task, via the viscoelastic control model. We illustrate through simulated data collected from the ARGoS simulator that a significant improvement in fault detection can be obtained by using the proposed method where compared to the conventional PCA-based methods (i.e., T 2 and Q).

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“…However, such approaches ignore the interaction between robots and therefore result in a misleading analysis. In the other hand, exogenous fault detection techniques were developed to inspect several robots simultaneously [8]. In other words, in such techniques a robot could detect errors that occurred in other robot components by taking into consideration the available information of its neighborhood in the swarm [9]- [11].…”

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.

show abstract

Adaptive data-driven error detection in swarm robotics with statistical classifiers

Cited by 24 publications

References 9 publications

To err is robotic, to tolerate immunological: fault detection in multirobot systems

To err is robotic, to tolerate immunological: fault detection in multirobot systems

Monitoring a robot swarm using a data-driven fault detection approach

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

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