AbstractThe Autonomic Computing paradigm was first presented almost 18 years ago as a 20-30 year long research agenda. Organizations like NASA, have explored the possibilities of using autonomic systems in their future missions due to vast distances experienced in space exploration. Planetary and mobile robots operate in hostile environments and because of their remote location, human intervention for repairs is not possible. Hardware devices, like mobile robots, are susceptible to internal and external environmental changes, which can lead to faults occurring. Some research has been conducted in terms of handling faults in mobile robots but there is no generic autonomic model that can be used for any type of system fault, in any type of mobile robot. This Thesis describes a generic autonomic architecture for use in developing systems for managing hardware faults in mobile robots. Using autonomic principles, this Thesis focuses on how to detect faults within a mobile robot and how specialised algorithms can be deployed to compensate for the faults discovered. The initial design of a generic architecture is developed using inspiration from the MAPE-K and IMD architectures. Case studies are presented that show three different fault scenarios that can occur within the effectors, sensors and power units of a mobile robot. The results from each of the Case Studies is used to create and refine a generic autonomic architecture that can be utilized for any general mobile robot setup for fault handling. A further Case Study is presented,
which exercises the generic autonomic architecture in order to demonstrate its utility.
This Thesis addresses the 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-managing strategy as part of its processes may result in the mobile robot continuing to function at a productive level. Research in this Thesis has provided insights into the shortcomings of existing robot design which is also discussed.
|Date of Award||May 2020|
|Supervisor||George Wilkie (Supervisor) & Roy Sterritt (Supervisor)|