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Efficient Design and Optimization of High-Speed Electronic System Interconnects Using Machine Learning Applications
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Author(s): A. Saravanan (SMK Fomra Institute of Technology, Chennai, India), S. Bathrinath (Kalasalingam Academy of Research and Education, Krishnankoil, India), Hari Banda (Villa College, Maldives), S. J. Suji Prasad (Kongu Engineering College, Erode, India), Jonnadula Narasimharao (CMR Technical Campus, Hyderabad, India)and Mohammed Ali H. (SRM Institute of Science and Technology, Ramapuram, India)
Copyright: 2024
Pages: 20
Source title:
Metaheuristics Algorithm and Optimization of Engineering and Complex Systems
Source Author(s)/Editor(s): Thanigaivelan R. (AKT Memorial College of Engineering and Technology, India), Suchithra M. (SRM Institute of Science and Technology, India), Kaliappan S. (KCG College of Technology, India)and Mothilal T. (KCG College of Technology, India)
DOI: 10.4018/979-8-3693-3314-3.ch014
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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.
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