An autonomic lift truck for a smart factory

Abstract

Rapid technological advancements have introduced the concept of Smart Factory (SF), which requires systems with high levels of flexibility, adaptability, and digitization. This the- sis explores the design and implementation of an autonomic Lift Truck to achieve these char- acteristics within a SF environment. The proposed system uses the MAPE-K (Monitoring, Analysis, Planning, Execution, and Knowledge) framework to create an autonomic system (AcS), which is a self-managing system that includes four characteristics: self-configuration, self-healing, self-optimization, and self-protection (Self-CHOP). The transformation of a Turtlebot3 into an autonomic lift truck, encompassing mechan- ical modifications, electrical system enhancements, and software integration is addressed in this research thesis. Also, the interaction of the forklift with the SF, focusing on its ability to map and adjust to layout changes (self-configuration), send alerts for human intervention during faults (self-healing), optimize energy usage based on demand (self-optimization), and prevent hardware and software failures (self-protection) are tackled out. Pilot tests demonstrate the effectiveness of the autonomic Lift Truck in bringing flexi- bility, adaptability, and digitization to the SF. Results indicate that the proposed system can dynamically adapt to changes in a factory layout, maintain operational continuity through self-healing mechanisms, optimize resource usage based on real-time demands, and protect operations against potential disruptions. Summarizing, this thesis contributes to the field of AcS by presenting one of the first implementations that integrates all the features of Self-CHOP within an Industry 4.0 context. It provides a fundamental framework for future research and development of advanced AcS into smart manufacturing environments