Machine Learning-Based Ball Throwing Machine: An Intelligent Approach to Precision and Adaptability

Abstract

Sports training and industrial settings have made extensive use of automated ball-throwing machines; however, conventional models' precision and adaptability are limited by their fixed mechanical parameters. This study introduces a novel machine learning-based ball-throwing system that uses AI-driven decision-making and real-time sensor data to dynamically modify its throwing parameters. To maximize accuracy, the system's three components—a Ball Propelling Unit (BPU), a Hydraulic Pitching Assembly (HPA), and a Computer Imaging Device (CID)—work together. Equipped with a gyroscope, LIDAR, and a PIXY2 camera, the CID uses machine learning algorithms to determine the best course, detect user position, and identify coloured patches for tracking. The BPU improves ball swing and trajectory stability by imparting controlled spin through a Rifled Barrel (RB) that is slew motor-controlled. The HPA provides the necessary force, dynamically adjusting it according to AI-predicted values. It is made up of a hydraulic pump, hydraulic valve, and hydraulic cylinder. The machine learning framework combines reinforcement learning to continuously improve accuracy through real-time feedback and supervised learning to evaluate historical throwing data. The system's self-adjusting capability, which lowers error margins and guarantees constant throwing accuracy, is demonstrated by experimental evaluations. With its intelligent and adaptable pitching experience, the suggested machine has a lot of potential for use in recreational and sports training environments. Potential future developments could concentrate on improving response time, extending machine learning algorithms, and adding real-time feedback systems.