Open Access. Powered by Scholars. Published by Universities.®

Engineering Commons

Open Access. Powered by Scholars. Published by Universities.®

Mechanical Engineering

Master's Theses (2009 -)

Theses/Dissertations

Autonomous

Publication Year

Articles 1 - 2 of 2

Full-Text Articles in Engineering

Performance Analysis Of Constant Speed Local Abstacle Avoidance Controller Using A Mpc Algorithym On Granular Terrain, Nicholas Haraus Oct 2017

Performance Analysis Of Constant Speed Local Abstacle Avoidance Controller Using A Mpc Algorithym On Granular Terrain, Nicholas Haraus

Master's Theses (2009 -)

A Model Predictive Control (MPC) LIDAR-based constant speed local obstacle avoidance algorithm has been implemented on rigid terrain and granular terrain in Chrono to examine the robustness of this control method. Provided LIDAR data as well as a target location, a vehicle can route itself around obstacles as it encounters them and arrive at an end goal via an optimal route. This research is one important step towards eventual implementation of autonomous vehicles capable of navigating on all terrains. Using Chrono, a multibody physics API, this controller has been tested on a complex multibody physics HMMWV model representing the plant …


Verification Of A Dual-State Extended Kalman Filter With Lidar-Enabled Autonomous Hazard-Detection For Planetary Landers, Peter Joseph Jorgensen Apr 2015

Verification Of A Dual-State Extended Kalman Filter With Lidar-Enabled Autonomous Hazard-Detection For Planetary Landers, Peter Joseph Jorgensen

Master's Theses (2009 -)

This thesis presents a mathematical model for a LIDAR-enabled Terrain- and Hazard-Relative Navigation sensor and the design and implementation of a dual-state extended Kalman filter. The extended Kalman filter equations are presented in summary. Mathematical models for an altimeter, a velocimeter, a star tracker, and a lidar-enabled mapping/tracking sensor are presented in depth. An explanation of the software designed for computer simulation is included. It is proved through this analysis that, when implemented as part of a well-tuned extended Kalman Filter and in combination with other sensors, the proposed model for a lidar-enabled mapping/tracking sensor significantly reduces estimation error. This …