Autonomic architecture for fault handling in mobile robots
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ORIGINAL PAPER
Autonomic architecture for fault handling in mobile robots Martin Doran1
· Roy Sterritt1
· George Wilkie1
Received: 16 February 2019 / Accepted: 11 March 2020 © The Author(s) 2020
Abstract This paper describes a generic autonomic architecture for use in developing systems for managing hardware faults in mobile robots. The method by which the generic architecture was developed is also described. Using autonomic principles, we focused on how to detect faults within a mobile robot and how specialized algorithms can be deployed to compensate for the faults discovered. We design the foundation of a generic architecture using the elements found in the MAPE-K and IMD architectures. We present case studies that show three different fault scenarios that can occur within the effectors, sensors and power units of a mobile robot. For each case study, we have developed algorithms for monitoring and analyzing data stored from previous tasks completed by the robot. We use the results from the case studies to create and refine a generic autonomic architecture that can be utilized for any general mobile robot setup for fault detection and fault compensation. We then describe a further case study which exercises the generic autonomic architecture in order to demonstrate its utility. Our proposal addresses fundamental challenges in operating remote mobile robots with little or no human intervention. If a fault does occur within the mobile robot during field operations, then having a self-automated strategy as part of its processes may result in the mobile robot continuing to function at a productive level. Our research has provided insights into the shortcomings of existing robot design which is also discussed. Keywords Autonomic · NASA · Robots · Faults
1 Introduction For a mobile robot to complete its tasks, it relies heavily on the performance of its hardware components. A mobile robot needs to be aware of the behavior of its components, and they are functioning within established parameters. Development of a self-diagnostic system is important, so that the mobile robot can recognize the condition of each of its components [1]. Fault detection has been in development for mobile robots since the 1970s. The field of fault detection and isolation (FDI) [2] has adapted the use of filter detectors based on Kalman filtering, to detect inaccuracies in mobile robot functions over time [3]. The use of sensor fusion [4] has also been adapted to compare expected performance models of
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Martin Doran [email protected] Roy Sterritt [email protected] George Wilkie [email protected]
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normal sensor outputs with that of outputs from the actual mobile robot sensors. Classification and detection of faults can be established using techniques such as situation analysis [5]. Recognition of behavioral anomalies can be interpreted as symptoms of possible faults within the system. Other techniques such as redundant information statistics [6], look for subtle changes and deviations from normal execution to detect fa
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