1 / 10

MATLAB Program for a Vehicle-Tracking Robotic Car

This project presents a MATLAB-based simulation for a vehicle tracking robotic car, designed for potential use on lunar or Martian surfaces. Developed under the NASA SHARP High School Apprenticeship Program, this program focuses on adaptive cruise control, incorporating a 1-D model for vehicle dynamics and an expanded 2-D model that includes steering angles. The simulation accounts for various forces such as engine force, wind drag, and rolling friction, enabling greater accuracy in navigation and control. Key enhancements include multiple braking levels and maximum acceleration measures to ensure effective vehicle operation.

ellery
Télécharger la présentation

MATLAB Program for a Vehicle-Tracking Robotic Car

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. MATLAB Program for a Vehicle-Tracking Robotic Car Allen Lin Rutgers University NASA SHARP High School Apprenticeship Program mentored by Dr. Baruh

  2. Background • Possible use on Moon or Mars • Adaptive cruise control • MATLAB

  3. 1-D Model Figure 1: A diagram of the forces acting on the vehicle in the simulation. The engine force (Fb) is determined by the forces of spring and dashpot in between the two cars. Fb = m * C1 * (D1 – D2 – Ddes) + m * C2 * (V1 – V2)

  4. Spring and Dashpot Constants • 4000 lbs, mini-van size • Larger – more accurate • Smaller – less force oscillation

  5. Additions • Three levels of braking • Max acceleration • Wind drag • Rolling friction – steady state error • Random error in distance apart, linear fit to determine velocity

  6. 2–D Model • With 1–D model, one variable: velocity, one degree of freedom: position • With 2-D model, two variables: steering angle, velocity; three degrees of freedom: x-position, y-position, angle relative to axes

More Related