Difference between revisions of "2020WinterTeam7"

From MAE/ECE 148 - Introduction to Autonomous Vehicles
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(Autonomous Driving using gradient descent)
(Autonomous Driving using gradient descent)
Line 27: Line 27:
  
 
<h4>The General Idea</h4>
 
<h4>The General Idea</h4>
The robot moves to a lower energy configuration and energy is minimized by following the negative gradient of the potential energy function
+
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.
  
 
<h4>Artificial Potential Field Methods</h4>
 
<h4>Artificial Potential Field Methods</h4>
  
 
<h5>The Attractive Potential</h5>
 
<h5>The Attractive Potential</h5>
 +
– Uatt is the “attractive” potential --- move to the goal
  
 
<h5>The Repulsive Potential</h5>
 
<h5>The Repulsive Potential</h5>
 +
– Urep is the “repulsive” potential --- avoid obstacles
  
 
<h5>Total Potential Function</h5>
 
<h5>Total Potential Function</h5>
 +
– Uatt is the “attractive” potential --- move to the goal
 +
– Urep is the “repulsive” potential --- avoid obstacles
  
 
<h4> Gradient Descent </h4>
 
<h4> Gradient Descent </h4>

Revision as of 10:35, 20 March 2020


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

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

Challenges

Conclusion

Project Links