Intelligent Selection of Mobility Systems For Unmanned Ground Vehicles Through Machine Learning

Objective: The primary objective of this study is to enhance the selection process of mobility systems for unmanned ground vehicles (UGVs) by leveraging machine learning techniques. Specifically, it aims to identify the most suitable mobility systems that align with mission requirements and user needs while optimizing performance across diverse terrains. Theoretical Framework: This research is grounded in theories of systems engineering and decision-making processes related to vehicle design. It builds on the premise that mobility systems are key determinants of vehicle performance, affecting aspects such as energy efficiency, maneuverability, and load-carrying capacity. The integration of machine learning within the design process represents a shift from traditional methodologies, facilitating a data-driven approach to system selection. Method: The study employed a machine learning framework to analyze UGV mobility systems by addressing feedback from five key questions. Various classification algorithms were utilized, including Random Forest, Naive Bayes, Support Vector Machines, and k-Nearest Neighbors. The performance of these algorithms was evaluated based on accuracy metrics such as precision, recall, and F1 scores, allowing for a comprehensive assessment of their efficacy in predicting suitable mobility systems. Results and Discussion: The findings highlight that the Random Forest algorithm outperformed others with an accuracy of 98.7%, indicating its effectiveness in classifying suitable mobility systems for UGVs. The research discusses the implications of employing machine learning in this context, suggesting that it can streamline the design process by quickly identifying strong candidates for mobility systems. Challenges associated with the complexity of UGV parameters, and the importance of tailored mobility solutions are also explored. Research Implications: This study underscores the significance of integrating machine learning into the design and selection of UGV mobility systems, offering a new perspective on improving operational effectiveness. It provides insights for engineers and researchers in the field of unmanned systems, suggesting a paradigm shift that prioritizes data-driven decision-making over traditional approaches. Originality/Value: This research contributes original insights by introducing a novel approach to mobility system selection for UGVs through machine learning. It adds value by demonstrating the potential for increased accuracy and efficiency in system design, which could lead to enhanced mission success and reduced costs associated with design modifications. The study bridges a gap in existing literature by combining mobility system analysis with advanced computational techniques, paving the way for future advancements in unmanned vehicle design.