2019FallTeam8

From MAE/ECE 148 - Introduction to Autonomous Vehicles
Jump to navigation Jump to search

Team Members

Fall 2018

  • Lovpreet Hansra, Computer Science BS
  • Adam Porter, Electrical Engineering BS
  • Luan Nguyen, Aerospace Engineering BS


The Idea

Our goal was to provide our car with particular GPS coordinates, have the car navigate to the destination coordinates by using the magnetometer within the IMU, and then search for a particular object within a given radius. We utilized computer vision to distinguish between objects and find the desired one while the car circumnavigates the area.

Must Have

  • Object Recognition
  • GPS Navigation
  • Working IMU interface

Nice to Have

  • Obstacle avoidance
  • Search within specified radius
  • IMU communicating directly to the Jetson without the Arduino

Mechanical Components

Base Plate

Our base plate model

We went with a relatively simple base plate to accommodate all of our hardware and help a little with cable management.

Camera Mount

Camera Mount
Camera Mount Base

We wanted an adjustable camera mount to experiment with different angles so we printed 2 separate pieces to mount the camera.

Ultrasound Casings

UltrasoundPt1.png
UltrasoundPt2.png

We printed some casings for our ultrasound sensors incase the car had any collisions.

Hardware

Devices

IMU

Ultrasonic Sensors

USB GPS

Arduino Mega

Wiring

  • Insert picture and maybe some explanations

Software

For object recognition, we implemented a YOLO algorithm trained using the COCO dataset which classifies 80 different classes of objects. The yolo algorithm essentially using bounding boxes to locate and classify objects in a given image. To classify the objects, I used a CNN using the darknet framework, but implemented using pytorch. The object detection model uses leaky ReLU as its activation function, and has a convolutional layer and pooling layer at every step. The last layers are fully connected and use softmax to get probabilities for each class. This is a commonly used and efficient sequence to classify images of objects. The yolo algorithm uses a bounding box method to detect multiple objects in a frame. The image is broken up into a grid and each section of the grid has a specified number of boxes and each box can detect an object. The box that contains the center of the object is the box responsible for classifying the object. For this implementation, I started with open source code and expanded it to work for our project and with the Jetson. The algorithm must be run in conjunction with the DonkeyCar program. The way we integrated the object detection was to have the program write to a file and create a donkey car part that continuously read the file for changes. When they donkey car reads that the specified object has been found, it stops moving, and if the object is removed from the frame it starts searching for the object again.

Steps to Reproduce Our Result

1. Build a Donkey Car 2. Implement GPS navigation 3. Implement Object Recognition 4. Specify object to find 5. Run Object Recognition in conjunction with Donkey Car with integrated GPS navigation

Videos of Final Result

Conclusion

Possible Improvements

Our project is a pretty good base model for finding an object in some specified location. Improvements that could be made would be developing an efficient search algorithm for the 2 meter radius that GPS navigation allows us to reach.

Project Links

Resources