Artificial Immune System Based Robot Anomaly Detection Engine for Fault Tolerant Robots

Jakimovski, Bojan; Maehle, Erik

doi:10.1007/978-3-540-69295-9_16

Cited by 16 publications

(8 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Authors categorized various negative selection algorithms. lakimovski and Maehle [3] developed robot anomaly detection methodology based on negative selection algorithm and clonal selection theory. Fuzzy logic was applied for information processing.…”

Section: Negative Selection Algorithmmentioning

confidence: 99%

Fault detection in robot sensors using negative selection algorithm

Khan

Hussain

Bakhtair

et al. 2014

2014 9th International Conference on Computer Science &Amp; Education

View full text Add to dashboard Cite

This paper describes the methodology for fault detection in autonomous mobile robots. In this, artificial immune system based approach; a set of fault detectors is generated using negative selection algorithm which is responsible for detecting faulty behavior of system. The idea is based on the self-non self discrimination observed in biological immune system. The feasibility of scheme is implemented on sensors of simulated mobile robot.

show abstract

Section: Negative Selection Algorithmmentioning

confidence: 99%

Fault detection in robot sensors using negative selection algorithm

Khan

Hussain

Bakhtair

et al. 2014

2014 9th International Conference on Computer Science &Amp; Education

View full text Add to dashboard Cite

show abstract

“…A method inspired in the Natural Immune System for Robot Anomaly Detection, with focus in detecting failures in autonomous robot systems is proposed in [18]. The system was tested with the aid of the robot OSCAR [19], which has 6 legs with 3 motors per leg.…”

Section: Related Workmentioning

confidence: 99%

Detecting Robotic Anomalies using RobotChain

Lopes

Alexandre

2019

2019 IEEE International Conference on Autonomous Robot Systems and Competitions (ICARSC)

View full text Add to dashboard Cite

Robotic events can provide notable amounts of information regarding a robot's status, which can be extrapolated to detect productivity, anomalies, malfunctions and used for monitorization. However, when problems occur in sensitive environments like a factory, the logs of a machine may be discarded because they are susceptible to chances and malicious intents. In this paper we propose to use RobotChain for anomaly detection. RobotChain is a method to securely register robotic events, using a blockchain, which ensures that once an event gets registered on it, it's secured and cannot be tampered with. We show how this system can be leveraged with the module for anomaly detection, that uses the information contained on the blockchain to detect anomalies on a UR3 robot.

show abstract

“…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

View full text Add to dashboard Cite

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).

show abstract

Artificial Immune System Based Robot Anomaly Detection Engine for Fault Tolerant Robots

Cited by 16 publications

References 6 publications

Fault detection in robot sensors using negative selection algorithm

Fault detection in robot sensors using negative selection algorithm

Detecting Robotic Anomalies using RobotChain

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

Contact Info

Product

Resources

About