‹ MAE 148 — Introduction to Autonomous Vehicles · Maurício de Oliveira

2022SpringTeam3

Team Members

Project Overview

Our project used tesseract and EAST text detection with openCV to detect text, then which the car will go and pick up the specified object using a model trained with the YOLO algorithm. For example, if we show the car the word apple, it will go and pick up an apple. We used openCV and imutils library to control the camera, pyVESC to control the vesc and the motor, and finally the python servo motor library to control the servo motor to pick up the actual item. We used a brushless DC motor, VESC, Jetson Nano, 1080p webcam, and a servo motor.

Main Components

Physical Setup

Car

Progress

Car 3.jpg

Final Product

Car 2.jpg

Schematic

Team3 circ diag.png

Implementation

Software and Hardware List

The following are needed to complete this project.

Source code

The source code of the project can be found here. Good luck running it :): https://github.com/SikGek/ECE_148_Final

Steps Overview

Results

Our team was able to successfully have it retrieve 3 different types of items, although 2 is shown for saving time. The throttle is slow to compensate for the delay in camera feed due to poor processing power of the Jetson Nano.

Tesseract is painfully slow due to the model being trained for all the characters in the alphabet, while we reduced the YOLO model to the smallest size for best possible performance.

Gantt chart

Gantt chart3.jpeg

Demonstrations

Entire Project at Work on an Apple and a Banana

{{#widget:YouTube|id=U7MZx0XJJCI}}

The Camera feed to Showcase that it only detects one object

{{#widget:YouTube|id=RCgmAcYuXkE}}

Challenges

Challenge Solution
Inconsistent object detection Higher resolution camera improved results
Little time to develop in ROS Used python libraries instead
Jetson doesn't have enough power to power the servo motor Use i2c power switch
Poor and slow object detection Training our specific model

Potential Improvement

Weekly Presentations

Final Project Proposal
Progress Week 8
Progress Week 9
Final Presentation

Reference

We would like to give special thanks to:

Disclaimer

I kinda copied team 7's format, hope that's ok. Their project was very sick, go press a like and subscribe to them. Thank you. ^^7


Recovered from the original course wiki (server backup, August 2024).