Wall-E

Inspiration

With an interest in embedded systems, we wanted to create something that can have a positive impact. With many dangerous and abandoned buildings, they may need to be surveyed, however this can be risky to be done in person due to chances of falling bricks, hazzardous material etc.

What it does

Wall-E is an autonomous vehicle which maps out the obstacles and walls in a room. It has possible use cases in mapping out abandoned/dangerous buildings. An ultrasonic sensor rotates via the use of a servo to detect walls and obstacles in a room, this data, as well as the relative position of Wall-E is sent to a user interface which displays the map of the room as it's being created.

How we built it

We used a base with 2 motors and a L298N motor driver, as well as a ultrasonic sensor to detect the walls, and a servo to allow the ultrasonic sensor to rotate, allowing it to check for corners. An ESP32 was used as the controller of the system; we chose this due to the fact it allows easy wireless communication. For the user interface, Pygame was used, data is sent from the ESP32 to the interface via MQTT.

Challenges we ran into

We were off to a smoking start, and then a shock, quite literally. After connecting the battery to the motor driver, the high current caused the wires to smoke and melt, before slightly electrocuting us. This was the start of our troubles. We also initially wanted to use a Raspberry Pi however, we struggled with getting the RPi connected to the University network and therefore struggled to actually access it. Another big issue which took alot of time was trying to set MQTT up, we initially tried hosting the broker server locally, however university firewall rules prevented this. Therefore, we switched to a public broker, but, the Python MQTT library we were using had bugs in its latest version, and we had to downgrade, something we only realized after alot of time was put into debugging it.

Accomplishments that we're proud of

We're proud of the fact we managed to get rear wheel steering implemented nearly perfectly first try.

What we learned

This was our first time learning how to use MQTT as well as doing a larger hardware/embedded systems project.

What's next for Wall-E

Wall-E could be upgraded, via the use of a Raspberry Pi and a camera module, an AI object detection model can be deployed, allowing object classification which would be useful in the use case of exploring dangerous buildings. In addition to this, there are issues with the vehicle moving on certain materials due to only having two wheels, so 2 more wheels could be added and a better steering system designed, possibly using servos on the front wheels to control the steering.