Sensor fault detection and diagnosis for autonomous systems

Eliahu Khalastchi, Meir Kalech, Lior Rokach

Research output: Contribution to conferencePaperpeer-review

24 Scopus citations

Abstract

Autonomous systems are usually equipped with sensors to sense the surrounding environment. The sensor readings are interpreted into beliefs upon which the robot decides how to act. Unfortunately, sensors are susceptible to faults. These faults might lead to task failure. Detecting these faults and diagnosing a fault's origin is an important task that should be performed quickly online. While other methods require a high fidelity model that describes the behavior of each component, we present a method that uses a structural model to successfully detect and diagnose sensor faults online. We experiment our method with a laboratory robot Roboticanl and a flight simulator FlightGear. We show that our method outperforms previous methods in terms of fault detection and provides an accurate diagnosis.

Original languageEnglish
Pages15-22
Number of pages8
StatePublished - 1 Jan 2013
Event12th International Conference on Autonomous Agents and Multiagent Systems 2013, AAMAS 2013 - Saint Paul, MN, United States
Duration: 6 May 201310 May 2013

Conference

Conference12th International Conference on Autonomous Agents and Multiagent Systems 2013, AAMAS 2013
Country/TerritoryUnited States
CitySaint Paul, MN
Period6/05/1310/05/13

Keywords

  • Fault detection
  • Model-Based Diagnosis
  • Robotics
  • Sensors
  • UAV

ASJC Scopus subject areas

  • Artificial Intelligence

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