Physical Sciences and Mathematics Commons^™

Optimization Of Orbital Trajectories Using Neuroevolution Of Augmenting Topologies, Nathan Wetherell

University Scholar Projects

This project aims to determine the feasibility of using NeuroEvolution of Augmenting Topologies (NEAT), an advanced neural network evolution scheme, to optimize orbital transfer trajectories. More specifically, this project compares a genetically evolved neural network to a standard Hohmann transfer between Earth and Mars. To test these two methods, an N-body simulation environment was created to accurately determine the result of gravitational interactions on a theoretical spacecraft when combined with planned engine burns. Once created, this simulation environment was used to train the neural networks created using the NEAT Python module. A genetic algorithm was used to modify the topology …

Evaluating Driving Performance Of A Novel Behavior Planning Model On Connected Autonomous Vehicles, Keyur Shah

Honors Scholar Theses

Many current algorithms and approaches in autonomous driving attempt to solve the "trajectory generation" or "trajectory following” problems: given a target behavior (e.g. stay in the current lane at the speed limit or change lane), what trajectory should the vehicle follow, and what inputs should the driving agent apply to the throttle and brake to achieve this trajectory? In this work, we instead focus on the “behavior planning” problem—specifically, should an autonomous vehicle change lane or keep lane given the current state of the system?

In addition, current theory mainly focuses on single-vehicle systems, where vehicles do not communicate with …