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

Bojan Jakimovski; Erik Maehle

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

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

Bojan Jakimovski, Erik Maehle

Institute of Computer Engineering

Abstract

Robot anomaly detection method described in this paper uses an approach inspired by an immune system for detecting failures within autonomous robot system. The concept is based on self-nonself discrimination and clonal selection principles found within the natural immune system. The approach applies principles of fuzzy logic for representing and processing the information within the artificial immune system. Throughout the paper we explain the working principle of RADE (Robot Anomaly Detection Engine) approach and we show its practical effectiveness through several experimental test cases.

Original language	English
Title of host publication	ATC 2008: Autonomic and Trusted Computing
Number of pages	14
Volume	5060 LNCS
Publisher	Springer Verlag
Publication date	05.08.2008
Pages	177-190
ISBN (Print)	978-3-540-69294-2
ISBN (Electronic)	978-3-540-69295-9
DOIs	https://doi.org/10.1007/978-3-540-69295-9_16
Publication status	Published - 05.08.2008
Event	5th International Conference on Autonomic and Trusted Computing - Oslo, Norway Duration: 23.06.2008 → 25.06.2008 Conference number: 72795

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

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title = "Artificial Immune System Based Robot Anomaly Detection Engine for Fault Tolerant Robots",

abstract = "Robot anomaly detection method described in this paper uses an approach inspired by an immune system for detecting failures within autonomous robot system. The concept is based on self-nonself discrimination and clonal selection principles found within the natural immune system. The approach applies principles of fuzzy logic for representing and processing the information within the artificial immune system. Throughout the paper we explain the working principle of RADE (Robot Anomaly Detection Engine) approach and we show its practical effectiveness through several experimental test cases.",

author = "Bojan Jakimovski and Erik Maehle",

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doi = "10.1007/978-3-540-69295-9_16",

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series = "Lecture Notes in Computer Science ",

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note = "5th International Conference on Autonomic and Trusted Computing , ATC 2008 ; Conference date: 23-06-2008 Through 25-06-2008",

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Jakimovski, B & Maehle, E 2008, Artificial Immune System Based Robot Anomaly Detection Engine for Fault Tolerant Robots. in ATC 2008: Autonomic and Trusted Computing . vol. 5060 LNCS, Lecture Notes in Computer Science , vol. 5060 LNCS, Springer Verlag, pp. 177-190, 5th International Conference on Autonomic and Trusted Computing , Oslo, Norway, 23.06.08. https://doi.org/10.1007/978-3-540-69295-9_16

Artificial Immune System Based Robot Anomaly Detection Engine for Fault Tolerant Robots. / Jakimovski, Bojan; Maehle, Erik.
ATC 2008: Autonomic and Trusted Computing . Vol. 5060 LNCS Springer Verlag, 2008. p. 177-190 (Lecture Notes in Computer Science ; Vol. 5060 LNCS).

Research output: Chapters in Books/Reports/Conference Proceedings › Conference contribution › peer-review

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AB - Robot anomaly detection method described in this paper uses an approach inspired by an immune system for detecting failures within autonomous robot system. The concept is based on self-nonself discrimination and clonal selection principles found within the natural immune system. The approach applies principles of fuzzy logic for representing and processing the information within the artificial immune system. Throughout the paper we explain the working principle of RADE (Robot Anomaly Detection Engine) approach and we show its practical effectiveness through several experimental test cases.

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DO - 10.1007/978-3-540-69295-9_16

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T3 - Lecture Notes in Computer Science

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T2 - 5th International Conference on Autonomic and Trusted Computing

Y2 - 23 June 2008 through 25 June 2008

ER -

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

Abstract

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