Study on robotic projectile launching based on multi-factor analysis and parameter optimization

The precision of projectile launching mechanisms, which utilize counter-rotating friction wheels, is critical for system effec-tiveness. This study introduces a hybrid approach combining multi-physics simulation with an intelligent optimization algo-rithm to determine key design parameters. Initially, Finite Element Analysis (FEA) and kinematics simulations were conducted on a 3D model to generate a comprehensive dataset linking operational conditions to projectile dynamics. This dataset then served to train a neural network for velocity prediction. Subsequently, a genetic algorithm was implemented to optimize the friction coefficient and inter-wheel gap by targeting a desired exit velocity range. The proposed methodology successfully identifies optimal parameter configurations, offering a robust, data-driven solution to a complex design challenge.