Model-based fault diagnosis and reconfiguration of robot drives

Brandstötter, Mathias; Hofbaur, Michael; Steinbauer, Gerald; Wotawa, Franz

doi:10.1109/iros.2007.4399092

Cited by 17 publications

(18 citation statements)

References 8 publications

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“…First, the reconfiguration of the remaining computing resources due to the isolation of a specific component poses a challenge. In this respect, work on automated fault localization and program repair appears to be promising [17,18]. Second, the verification that fail-operational requirements can be met in all considered failover scenarios is an unsolved issue.…”

Section: Generation Of Test Scenarios and Test Datamentioning

confidence: 99%

Quality assurance methodologies for automated driving

Wotawa

Peischl

Klück

et al. 2018

Elektrotech. Inftech.

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For safety critical systems like cars, trains, or airplanes quality assurance methods and techniques are crucial for preventing situations that may harm people. The case of automated driving represents the next level of safety critical systems where additional challenges arise. This includes the question of how to assure that artificial intelligence and machine learning based systems fulfill safety criticality requirements under all potential conditions and situations that may emerge during operation. In this paper, we first review simulation-based verification and validation methods for such systems and afterwards discuss necessary requirements and future research challenges that have to be solved in order to bring automated driving into practice without compromising safety requirements.

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Section: Generation Of Test Scenarios and Test Datamentioning

confidence: 99%

Quality assurance methodologies for automated driving

Wotawa

Peischl

Klück

et al. 2018

Elektrotech. Inftech.

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“…In the development we utilized our experience in diagnosis systems and results of previous work of other colleagues and us, such as robotics software diagnosis [2], [3], [4], hardware repair [5] and sensor validation [6].…”

Section: Introductionmentioning

confidence: 99%

An integrated model-based diagnosis and repair architecture for ROS-based robot systems

Zaman

Steinbauer

Mäurer

et al. 2013

2013 IEEE International Conference on Robotics and Automation

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Autonomous robots are artifacts that comprise a significant number of heterogeneous hardware and software components and interact with dynamic environments. There fore, there is always a chance of faults at run-time that negatively affect the reliability of the system. In this paper we present a novel diagnosis and repair architecture for ROS-based robot systems. It is an extension to the existing ROS diagnostics stack and follows a model-based diagnosis and repair approach.In the paper we discuss the integrated diagnosis and repair architecture in detail. Moreover, we show its application to an example robot system and report first experimental results. The presented work provides three major contributions: a combi nation of diagnosis and repair, the integration of hardware and software, and the integration into ROS.

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“…KINEMATICS REASONING The kinematics reasoning unit represents the key component for our control architecture. Its functionality builds upon the qualitative kinematic constraints that we introduced in [1] for Mecanum wheels. These constraints provide the basis to deduce (on-line) the space of admissible and controllable motions B for a robot drive with specific geometric configuration and mode of operation/failure.…”

Section: Introductionmentioning

confidence: 99%

On-line kinematics reasoning for reconfigurable robot drives

Hofbaur

Brandstötter

Schörghuber

et al. 2010

2010 IEEE International Conference on Robotics and Automation

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The control system for a mobile robot typically assumes fixed kinematics according to the drive's geometry and functionality. Faults in the system, for example a blocked steering actuator, will then lead to an undesired behaviour, unless one takes care of specific single and/or multiple faults explicitly. We present a novel model-programmed procedure for on-line kinematics reasoning that allows a robot to deduce the (inverse)-kinematics of the drive and also its kinematic abilities for the specific modes of operation and some falt modes during operation. As a consequence, we can reconfigure a robot drive to compensate for some faults and also inform a higher level control system about changed mobility capabilities of a robot. Being fault tolerant is, however, only one advantage of our approach that derives the kinematics control strategy from a geometric and functional model of the drive. We can easily adapt the controller for various robot drives, handle drives that change their geometry and functionality during run-time and also provide the basis for a flexible control scheme for self-configuring multi-robot systems.

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Model-based fault diagnosis and reconfiguration of robot drives

Cited by 17 publications

References 8 publications

Quality assurance methodologies for automated driving

Quality assurance methodologies for automated driving

An integrated model-based diagnosis and repair architecture for ROS-based robot systems

On-line kinematics reasoning for reconfigurable robot drives

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