Competition Robot Photos & Team Video

Our team video on the project can be found here, but some quick photos are below:

Competition Day!

Guess what…The Young Pi-oneers came 10th overall for Young Novices in Pi Wars 2024! We also came 3rd in the Zombie Apocalypse (shooting) challenge!!

Our robot competed in all seven of the challenges which included: Eco disaster (pushing “toxic” barrels into recovery zones), Minesweeper (driving from randomly lit up to square to randomly lit up square to diffuse the bombs), Zombie Apocalypse (shooting zombie targets), Pi Noon (1:1 robot duels to pop opponents balloons), Escape Route (driving through a maze blindly), The Temple of Doom (driving over an obstacle course) and Lava Palava (following a line autonomously). We had to adapt the robot from challenge to challenge by utilising the various attachments that we had designed and built. 

On the day we had 2 main technical problems, both of which I believe came down to pure bad luck. The less substantial of the two was a loose wire which meant one of the four motors could not go in one direction, which meant it was difficult to turn and drive. The other problem we encountered was during Eco disaster when we accidentally drove quickly into a corner and our grabber snapped in half. This meant that we had to continue by pushing the barrels with the front of our robot, although that was tricky and sometimes caused it to get stuck.

As I was driving the robot, nerves were my only feeling as there were a lot of people watching and cheering. Competition day was full of mixed emotions because for one thing it was fun and a huge weight was lifted off my shoulders, but in another way it was sort of sad because the Pi Wars fun was over. The main thing that I enjoyed was the atmosphere of all robot enthusiasts, all cheering for all the robots to succeed. 

When we won the 10th place award I mostly felt confused because I thought we had not done that well, but then I felt happy as I collected the prize. It seems that scoring consistently across the challenges had helped us get a good total score.

During the build up to the competition I've improved at coding, especially in Python using UART communication protocol between microcontrollers. The most difficult coding challenge was line following and I learnt how to use Bing Chat (AI Co-Pilot) to generate code. I have gained a lot more experience soldering in order to make the robot's circuits. I have also refined my 3D design/3D printing in order to create parts. I've also practised writing blog articles to share my experience.

All of the team contributed and really enjoyed experience. Some of the feedback from in the team was:

During the build process I learnt so much, including soldering and wiring, coding, designing and 3D printing, working as a team and what goes into building a robot.

On the day of the event I enjoyed seeing other people’s robots to see the techniques and designs that they had used. I also enjoyed watching our robot attempt the various challenging challenges. It was also good to hang out with my friends and family who supported us during the process.

If we were to do it again I think the skills I have learned and the ideas gathered from the other teams we would do even better

Overall, an amazing experience!

When do we sign up for next year? 😃

Ready, Aim and Fire …

Today’s task was trying to get the robot’s gun to fire. 

In the beginning it didn’t fire and it took us a while to figure out one on the servo wasn’t working. We changed it for a different one which solved our first problem! The gun fired, but it wouldn’t reload every time so that’s a new problem we need to overcome. The team will hopefully coming up with solutions. 

Another problem that we did solve was that the vertical  tilt arm wouldn’t go all the way back. So we had to redesign the part on Tinkercad and reprint on the 3D printer and replace it on the robot.

After overcoming these problems we have a robot that can aim and fire (5 rounds in a row)! 

The journey to building a grabber

Step 1, find a grabber on Thingiverse. Step 2, print the grabber. Step 3, go back to step 1.

School have provided us with extra DT drop in sessions in which we have prototyped parts for the actual robot. One area we have focused on is the grabber. We have made a number of grabbers that have small issues, making them a failure 🙁. However we have learned different methods of manufacturing, including FDM 3D Printing ("Fused deposition modelling") and Laser cutting of sheet plastic. We had some problems with the laser cutter meaning we had go round twice (or more!) to get through the plastic.

We looked at two different styles

1. Pincer arms, e.g. https://www.thingiverse.com/thing:5149951

2. Rack an pinion 

 

Each operated by an SG90 micro servo

In the end we decided rack and pinion was the way to go. The single direction of motion was looking simpler and stronger for grabbing barrels.

We went through multiple iterations, trying different arm lengths, but now have a design that seems to work. 

Starting to follow the line

 

We started with a new approach to line following. Initially we attempted to use 5 IR sensors but we found there was too much overlap in the reflection of the IR beams, causing inaccurate readings. We switched to 3 sensors, but still had problems. Firstly we added fairings to block the overlap. Then we switched to following a white line on a black background (luckily the kitchen table was black!). These changes resulted in setup the could reliably detect the line. Now we needed so code....

We started by creating our own algorithm which gave each of the line sensors a number which is what we used to to set the motor steering angle to. However we found that we kept overshooting the sharp turns, so we added a small code snippet to make it reverse when we overshot the end of the line. This worked but was not very reliable.

We had read previously (See book review on "Robotics at Home with Raspberry Pi Pico") about using a PID algorithm. However it sounded complicated to code, so was asked Bing Co-Pilot "write the code in CircuitPython to do line following using PID algorithm with 3 digital line sensors, driving 2 motors". We modified this to use out own motor setup functions and watched it wiggle down the line. After a bit of tuning, success! We had a pretty reliable line following robot.

import time

import board

import pulseio

from digitalio import DigitalInOut, Direction, Pull

# Initialize the motors

left_motor = pulseio.PWMOut(board.D9, frequency=50)

right_motor = pulseio.PWMOut(board.D10, frequency=50)

# Initialize the line sensors

line_sensors = [DigitalInOut(board.D2), DigitalInOut(board.D3), DigitalInOut(board.D4)]

for sensor in line_sensors:

    sensor.direction = Direction.INPUT

    sensor.pull = Pull.UP

# PID constants

Kp = 1.0

Ki = 0.0

Kd = 0.0

# PID variables

integral = 0.0

previous_error = 0.0

while True:

    # Read the line sensors

    line_readings = [not sensor.value for sensor in line_sensors]

    # Calculate the error

    error = line_readings[0] - line_readings[2]

    # Calculate the PID terms

    proportional = error

    integral += error

    derivative = error - previous_error

    # Calculate the motor speeds

    left_speed = 0.5 + Kp*proportional + Ki*integral + Kd*derivative

    right_speed = 0.5 - Kp*proportional - Ki*integral - Kd*derivative

    # Set the motor speeds

    left_motor.duty_cycle = int(left_speed * 65535)

    right_motor.duty_cycle = int(right_speed * 65535)

    # Update the previous error

    previous_error = error

    # Wait for a short period of time

    time.sleep(0.01)

Tank Style Steering with the GamePad

We were thinking about having a new method of control for our robot, of which we concluded that we wanted to try using both analogue joysticks on our GamePad controller. We had already decided to use the messages from an EP32, that was paired via Bluetooth to the controller.  The ESP32 was running Bluepad32 for the complicated stuff. The commands from the controller were being sent as JSON over a UART/serial interface to the Raspberry Pi Pico. The Pico is the mastermind / brains behind the operations of controlling out actual robot hardware. The messages told us what the event from the GamePad was (e.g. Button pressed) and the analogue values / position of the joysticks at the time. In CircuitPython code, we parsed the message and used the values to set the motor speeds as needed. The Y value from the left joystick was used for both the left side motors, and the Y value from the right joystick was used for both the right motors. The allowed the robots to be steered like a tank with both sides being controlled independently. This gave the ability to turn more sharply - almost spinning on the spot - by driving the motors sets in opposite direction.  Fun driving times!

Off to the range - working out how to shoot zombies

The Christmas holidays proved a good time to catch-up with cousins who had by co-incidence been looking at how to build an automated firing nerf gun. Several YouTube video links later, we had plenty of laughs and ideas 

https://www.youtube.com/watch?v=3Ma5ZCZVQRs

https://www.youtube.com/watch?v=S3CwzkT6cK4

https://www.youtube.com/watch?v=GoZubkaNvcE

https://www.youtube.com/watch?v=PC-WdQGNNj4

The most promising one for inspiration seemed to be https://www.littlefrenchkev.com/bluetooth-nerf-turret.  However by co-incidence we were also able to start cannibalising a real nerf gun for parts.... disassembly promptly started.

  

We had the start of a firing mechanism for the robot (plus a collection of random plastic parts).

Good progress, but.... how to load the bullets? how to attach it to the robot? how to aim it?  Still plenty to do.... 

P.S. Some links we found for options on "linear actuators" which we think could be useful for us making a pusher to move the bullets in to the gun

https://www.thingiverse.com/thing:3170748

https://www.thingiverse.com/thing:3335693

https://www.thingiverse.com/thing:3118744

https://grabcad.com/library/linear-motion-with-mg90s-servo-1