From MAE/ECE 148 - Introduction to Autonomous Vehicles
Revision as of 11:28, 20 March 2020 by Winter2020Team7 (talk | contribs) (Autonomous Driving using gradient descent)
Jump to: navigation, search

Project Overview

Our goal was to provide our car with particular GPS coordinates, have the car navigate to the destination coordinates by using GPS and RTK2 corrections to achieve centimeter accuracy. We utilized Potential Functions and Gradient descent algorithm to avoid obstacles and find the path through objects.

Main objectives of adaptive cruise control are:

1. GPS-RTK Navigation from source to the assigned destination

2. Connecting the two C099-F9P GPS-RTK2 modules using Odin (wifi)

3. Obstacle avoidance

Team Members

Chanyang Yim – Mechanical and Aerospace Engineering Department

Jiuqi Wang – Mechanical and Aerospace Engineering Department

Omid Hasanli – Electrical and Computer Engineering Department

Design and Assembly of Donkey

Plate and Camera Mount Design


Autonomous Driving using gradient descent

The General Idea

To build potential fields, so that the point that represents the robot is attracted by the goal and repelled by the obstacle region. The robot moves to a lower energy configuration and energy is minimized by following the negative gradient of the potential energy function.

Artificial Potential Field Methods

The Attractive Potential

– Uatt is the “attractive” potential --- move to the goal Figure 3.png

The Repulsive Potential

– Urep is the “repulsive” potential --- avoid obstacles

Total Potential Function

– Uatt is the “attractive” potential --- move to the goal – Urep is the “repulsive” potential --- avoid obstacles

Gradient Descent

Figure 1.png

Final Result

Input.png Output1.PNG



Project Links