IoT-Driven Solution for Time-Varying Kinematics: 'Reflected'Trajectory Prediction in Dynamic Grasping Tasks

Time-varying kinematics is a problem with extreme relevance in an increasingly automated world, with no more than a handful of papers studying it. For any industry nowadays to include automation within their pipeline, there will almost always be a need to have purpose-built robotics with expensive and elaborate infrastructure built around it. The reasoning for this is that any robotic-based automation implemented requires movement commands with pin-point precision to work reliably. The Kinematics of the robot is already a well-studied subject, however kinematics only ever reliably works on static object goals, not on moving object goals. This would pose a problem as any industry seeking to implement robotic automation would have no solution toward moving object goals. Additionally, traditional robotic automation incurs time-cost due to the necessity of static object goals during collection. Stopping the object goal or halting robot movement adds to the time overhead, impacting overall efficiency. To address these limitations, this research explores the integration of the Internet of Things (IoT) to develop a more robust "Reflected" trajectory prediction methodology. By leveraging IoT capabilities, it is possible to enhance the adaptability and responsiveness of robotic systems to dynamic grasping tasks involving moving object goals. The introduction of IoT design principles would also allow for a more robust range of robotics to be able to integrate the proposed system, as the computationally complex tasks might not be able to run well alone on a miniaturized robotic system.