Efficient Design and Optimization of High-Speed Electronic System Interconnects Using Machine Learning Applications

Abstract

This work presents a holistic framework for automating automated guided vehicles (AGVs) in industrial settings by using well-positioned sensors and sophisticated machine learning models. The AGV is put through rigorous testing along a variety of industrial pathways. It is outfitted with sensors such as wheel encoders, proximity sensors, ultrasonic sensors, and LIDAR. Microcontrollers in the high-speed electronic system enable real-time data processing and decision-making based on sensor inputs. For the purpose of anticipating impediments and maximising AGV routes, machine learning models such as decision trees (DT), artificial neural networks (ANN), support vector machines (SVM), and random forests (RF) are developed and assessed. Experiments showing accuracy, F1 score, precision, and recall show how well the integrated system is. The AGV is a prime example of effective route planning, obstacle avoidance, and navigation in busy industrial settings.