Thoughts and Theory

Deep Learning for Projectile Trajectory Modeling

A review of our recent paper on generating simulated data for deep learning-based trajectory modeling

A schematic of FCE-NN (Image by Authors)

This article was authored by Ayaan Haque and Sajiv Shah

In this article, we will review our recent work titled “Simulated Data Generation Through Algorithmic Force Coefficient Estimation for AI-Based Robotic Projectile Launch Modeling” by Sajiv Shah, Ayaan Haque, and Fei Liu. In this paper, we present FCE-NN, a novel method of modeling robotic launching of non-rigid objects using neural networks which are trained with supplemental simulated data, generated from algorithmic force coefficient estimation. This work has been accepted to ACIRS 2021. The paper is available in ArXiv, and the project website is here. We will first introduce our motivation and problem, then review the method, and finally present some short results and final thoughts.

Background

Motivation

As robots begin to become more advanced, they are required to mimic human-like behaviors and therefore must obtain a human-like skill-set. A large part of this involves interactions with objects, including manipulation and launching. Unfortunately, real-world…