Paper Predicting Flying Robot Dynamics with Deep Learning
With the rising importance of robotics in many industries, a way to quickly and easily test how robots move is required in order to help prevent damage to valuable research prototypes. With this in mind, Brian created an adaptable neural network that accurately predicts the movement of quadcopter robotic agents. It produces results within a very small margin of error, which is essential for accurate robot dynamics simulations. This neural network can also be expanded to encompass many more robots and applications given the requisite data.
Brian compiled his work and findings into a research paper that was published by the Journal of Student Research.Read his paper
Brian is a 17 year-old high schooler from Palo Alto, CA.
The Polygence experience was fantastic. My mentor was extremely interesting and helpful. He taught me a lot about his field of study, which I am very grateful for.