Autonomous Delivery Robot

Team

Project

We designed and assembled a self-driving delivery vehicle.

  • People and companies spend a lot of time delivering food, packages, and other items.
  • A self-driving delivery vehicle could save these people time and money.
  • We set out to design and assemble an autonomous vehicle that could safely and securely navigate around its environment to deliver a package to a user-defined destination.
Self driving delivery vehicle

System

Hardware

Our processor takes inputs from phones, a GPS sensor, a proximity sensor, and a camera to navigate around its environment. The processor drives DC motors and a lock on the box.

Hardware diagram

Software

Our vehicle navigates based on inputs from the GPS and TensorFlow model. The TensorFlow model scans images from a forward-facing camera for paths and branches in these paths.

Softeare diagram

Methods

  • We assembled the frame of the vehicle out of aluminum angles for their price and strength.
  • We chose a Raspberry Pi to drive the vehicle since it could run Tensorflow Lite, a deep learning image processing program.
  • The vehicle takes images from a forward-facing camera and scans them for the sidewalk path in front of the vehicle and for branches from the path.
  • We drive two geared DC motors through a PID controller. We adjust our target speed whenever our proximity sensor detects an obstacle in front of the vehicle.
  • We built an android app for our project’s UI. The app sends signals to a web server hosted by the Raspberry Pi.
Wiring Dagram

Conclusion

The vehicle and its peripherals all function as intended. However, we have not yet succeeded getting the vehicle to navigate to a location. This is due to difficulties and inconsistencies in our turning and branch recognition algorithms. However, we did accomplish the following:

  • Users can communicate with the vehicle through a custom android app
  • The vehicle knows its location at all times through a GPS sensor
  • The vehicle recognizes and avoids obstacles
  • The vehicle recognizes feasible paths through a custom Tensorflow Lite model
  • The vehicle met our price goal, costing less than $300

Once we improve our navigation algorithms, we will have a safe and functional self-driving vehicle that can deliver packages for people.