Autonomous Fault Detection for Performance Bugs in Component Based Robotic Systems

Abstract

We present a novel fault detection method for application in component-based robotic systems. In contrast to existing work, our method specifically addresses faults in the software system of the robot using a data-driven methodology which exploits the inter-process communication of the system. This enables an application of the approach without expert knowledge or availability of complex software models. We specifically focus on performance bugs, which slowly degrade the performance of the system and are thereby harder to detect but also most valuable for automatic recovery. Using a data set recorded on a RoboCup@Home platform we demonstrate the performance and applicability of our method and analyze the types of faults that can be detected by the method.

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Document Details

Document Type
Technical Report
Publication Date
Dec 01, 2016
Accession Number
AD1042903

Entities

People

  • Johannes Wienke
  • Sebastian Wrede

Tags

Communities of Interest

  • Autonomy

DTIC Thesaurus Topics

  • Anomaly Detection
  • Change Detection
  • Classification
  • Communication Channels
  • Control
  • Control Systems
  • Data Rate
  • Data Sets
  • Debugging
  • Detection
  • Detectors
  • Engineering
  • Human-Robot Interaction
  • Middleware
  • Robots
  • Software Development
  • Training

Fields of Study

  • Computer science
  • Engineering

Readers

  • Database Systems and Applications
  • Distributed Systems and Data Platform Development
  • Fault Tolerant Diagnosis of Black and White Balloon Isolation Tests Using ¥.

Technology Areas

  • AI & ML
  • AI & ML - Autonomous Systems
  • Autonomy
  • Autonomy - Autonomous System Control