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.
|Number of pages||26|
|Journal||Innovations in Systems and Software Engineering. A NASA Journal|
|Early online date||22 Apr 2020|
|Publication status||Published - 1 Dec 2020|
- Autonomic Computing