Delta
Maze league · Australia ·
RoboCup 2026
Document register
- Poster1 pagePublished
- Presentation videoYouTubePublished
- Bill of materials5 pagesPublished
- Team description paperNot shared
- Engineering journalNot shared
- Source code34 KB · GitHubPublished
Sharing each document is the team's decision. “Not shared” means this team chose not to publish it, or did not submit one — not that it is missing from the archive.
In their words
Our robot’s main capabilities include the ability to correct its alignment in the maze by using its two ToF sensors on each side to determine its skew in the maze and correct for this. It also incorporates a custom pivoting ToF sensor system for the front and rear ToF sensors allowing the sensors to always look flat at the walls even when training over bumps and when the robot will not be level with the ground.
Poster
Read the text of this document — 2138 words
Method, Production & Design Software
Robot Construction Navigation Algorithm
Teensy 4.1: OpenMV AE3: The core architecture of the software relies on a Finite State Machine (FSM) to manage all
The main controller that The robot holds two robot movements. An FSM is a system where each task a robot needs to complete is give
does all the processing for OpenMV AE3 cameras, a state. When the robot then needs to complete that task, it will then move into that state.
the robot is a teeny 4.1. one on the left and one on To then move out of this state there is then a set of conditions that need to be met for it to
This was chosen due to its the right. These cameras are change into a different state. At the center of our FSM is the mapping state; as the robot
superior speeds compared to used for victim detection traverses each tile, it executes the mapping algorithm to determine its next destination
other microcontrollers and and were chosen for the based on available empty tiles. This FSM is also in charge of the process for when a victim
its small form factor. small form factor. is detected and along with what task the robot as to perform on tile such as a blue tile.
MG996R Continuous Servo Adafruit VL53L4CD:
Motor: The robot holds eight
The robot holds four VL53L4CD distance sensors, To ensure precise timing, sensor data is captured using hardware interrupts. The main
MG996R continuous servo two on each side. These are software cycle runs every 100 milliseconds, during which different sensors are sampled at
motors to drive the wheels. used for sensing walls in all varying frequencies depending on their individual reading acquisition times. With different
These were chosen due directions and correcting the sensors being read every 5ms with the over 100ms sensor reading cycle.
to their ease of use and robots’ skew while driving
relatively small form factor. around the maze.
Adafruit VL53L4CX: 180-degree 9g Servo Motor:
DELTA
The robot holds two The robot uses three 9g
VL53L4CD distance sensors servo motors, two for the
for long distance sensing in LRF pivot and one for the
large open spaces of mazes. package delivery system. Our mapping strategy combines a standard left-wall-following approach with a custom
Limit Switches: Adafruit AS7341 colour navigation algorithm. The robot follows the left wall while traversing unvisited tiles.
The robot uses four limit sensor: However, upon reaching a previously visited tile, the custom algorithm takes over to
switches, two on the front The robot holds one colour determine the next move. This algorithm checks if any adjacent tiles connected to
The structural design and PCBs of the robot were created in and two on the rear. These sensor on the bottom of the the current visited tile remain unvisited. If an unvisited neighbour is found, the robot
Autodesk Fusion. Using Autodesk Fusion allowed for the robot to are used for centering front for detecting colour navigates to it and resumes the left-wall-following algorithm. If all adjacent tiles have
be visualised at any stage in the design before manufacturing it. The the robot and detecting floors such as red, black already been visited, the robot backtracks to the previous tile and repeats the check. This
robot was designed and manufactured completely in-house with the obstacles.
exception of the PCBs, which were designed in-house but sent to JLC
PCB to manufacture. The PCBs were still hand-soldered in-house.
GY-BNO055 9 axis IMU:
The robot uses one BNO055
or blue. This sensor was
chosen based on its large Brisbane BoysCollege - Rescue Maze backtracking loop continues until an unexplored path is found and eventually the robot
returns to the start tile. Throughout this process, crucial data such as tile coordinates,
range of colour channels (10 coloured tiles (e.g. blue or black tiles), and connectivity to other tiles.
This robot used many manufacturing techniques, including 3D IMU for detecting the channels).
printing on a Bambu Lab P1S and CNC milling on a Makera rotation of the robot in three
Carvera. axis.
The current robot uses nine major components, these can be seen on To optimize memory usage, the robot implements dynamic memory allocation for
the right. mapping instead of using a 3D array or static list. This approach ensures the robot
only consumes the exact amount of memory required, preventing waste. Furthermore,
utilizing dynamic allocation shifts the data storage from the stack to the heap. This is
crucial for the Teensy 4.1, which limits stack size to 16–32 KB while providing a much
larger ~512 KB heap. Within this heap space, each map tile is represented as a structured
PCBs data type (struct). Each tile struct stores its unique ID, coordinates, colour data (e.g.,
blue, black, silver), and victim presence, alongside pointers to adjacent tile structs to
form the map network.
The main PCB of the robot was designed completely in Autodesk Fusion because of the simplicity of
Victim Identification
designing every hardware part in the same program and the ability to visualise the 3D virtual PCB with
the robot assembly. This PCB was made to connect every signal from sensors and motors to the teensy 4.1
microcontroller. This board also houses the power circuit which consists of the 3.3V and 5V regulators. The
entire power circuit is located on the back of the board to allow for easy testing and fault nding when an
issue arises, especially when the issue is a short from power to ground. An image of the main PCBs layout
can be seen on the le.
The Light PCB of the robot was also
Abstract To detect the letter victims we are using a FOMO
MobileNetV2 object detection neural-network for
both the letter victims and the cognitive targets. This
designed completely in Autodesk Fusion DELTA is an Australian team from Brisbane Boys’ College competing in Robocup Junior Rescue Maze. We are a team of three members and have competed approach was chosen after seeing its capabilities in 2025
for the same reasons as the main PCB. in the Rescue Maze category for two years. Development of the current robot started in late 2025 where every team member has put in over 300 hours each at internationals where it was observed that many teams
The robot holds two of these light PCBs, to design, build, program and test our robot. DELTA competed in the Robocup Junior Rescue Maze International competition in Salvador, Brazil in 2025. that used a similar approach achieved very good results
one on the le and one on the right. This We have since competed in and achieved first place in the Robocup Junior Open Rescue Maze National Competition in 2025 and the Robocup Junior Open and stood out for their victim identification. This year
board lights the camera shot and allows Rescue Maze Brisbane Regional Competition in 2026. over 500 images were taken on an OpenMV AE3 camera
for the cameras to observe the victims The robot was made using industry standard technologies including - 3D printing, CNC Milling and laser cutting, along with the use of printed circuit of all the possible letter victims and these were used to
in almost exactly the same lighting boards (PCBs) to improve reliability and save time when constructing spare boards. train a model inside of Edge Impulse. Using the OpenMV
conditions no matter where the maze is Our team is made up of three members, Ethan Seymour, James Cousins, and Connor Duncan. AE3 camera helped replicate what the victims would
or how bright the lighting is. This board Ethan works on the hardware, including electrical design, structural design and manufacturing. Ethan has designed every part of the current robot himself look like on the robot by using the exact same hardware
includes a 15 individual white LEDs and has manufactured most parts himself with a CNC machine or 3D printer. James works on the main software and navigation algorithm and has coded as mounted on the robot (same camera and lens). These
and a potentiometer to ne tune the every part of the teensy code himself. Connor works on the victim detection software and has trained and coded the cognitive target recognition himself. images were then trained in Edge Impulse with 50
brightness of these LEDs. An image of epochs, a learning rate of 0.003 and a validation set size of
the light PCBs layout can be seen on the 20%. This was tuned from the default values to achieve a
right. higher precision score, recall score and therefore a higher
F1 score.
The image on the left shows the generated image features.
Pivoting LRFs The target detection works in two steps, it first uses blob detection to locate a target by looking for a large area of colour. Then it uses ring detection
code, which works in two main stages, to identify and interpret the target. The camera searches for the target area, locks onto the outer circle,
and keeps that circle position stable across frames so the readings do not jump around from one frame to the next. After the target has been
The pivoting LRF system was implemented to allow the robot to always see the wall straight on even when located, the code reads the five concentric rings by sampling several fixed points around each ring in the form of dots and checking which colour
the robot is tilted up or down. This system utilises a single 180-degree 9g servo motor. Image of team DELTA at the Robocup Image of team DELTA at the Robocup Scan the QR code to visit our website threshold each point matches. It then uses a voting method so that each ring gets a final colour decision based on the majority of samples. Once
This runs constantly throughout the robot’s running and prevents the robot from misidentifying where it is Junior National competition in 2025 International competition in 2025 for more information. all five rings have been classified, the script converts those colours into their assigned numbered values, adds them together, and uses the total to
located because it saw a wall at an angle. decide whether the target is real and if it should drop a package or not. The code also checks that the same ring pattern is seen repeatedly before
The robot uses two of these with one on the front and one on the back. Although using a similar system confirming the result, which helps reduce false detections.
may be useful on the side facing LRFs, space constraints did not allow for this on the current robot design.
Custom Silicone Wheels An image of this software detecting a target can be seen below
The robot runs four of the same custom molded silicone
Wheel Pivot
The Motor Pivot can be seen in the image above. This system consists of 2 needle roller bearings, one ball bearing
wheels. These wheels are made from a central 3D printed
hub made from PLA and Shore 25A silicone for the tire.
Silicone was chosen because of its superior grip due to its
elasticity. An added benefit of the silicone wheels is that
and an M8 bolt through the center. This bolt is fixed on one side and placed in a bearing on the other to allow it unlike some other choices, silicone does not stick to most
to pivot with minimal friction. The other two bearings are placed in the middle to allow the spacer to rotate. things and therefore will not pick up debris while navigating
The major reason for the use of a pivot was because of its ability to keep all wheels on the ground at the same around the maze. The chosen silicone has a hardness of
time. When a robot has fixed motors, scenarios arise where the robot cannot place all four wheels on the ground 25A on the Short Hardness Scale and was chosen for its
and will instead rock back and forth between two opposite wheels. With a motor pivot it is possible for the durability and grip. Other options were considered but were
motors to pivot down in these scenarios and therefore keep all four wheels in contact with the ground at all found to be too easily broken or not grippy enough.
times. The tire incorporates six large notches that allow for the
This design was chosen based on its high strength from the use of the M8 bolt through the center. When DELTA robot to climb stairs. The groves of the stairs catches on the
competed in 2025, it was observed that many teams had smaller pivots that at times would flex and would notches and gives the robot additional grip while climbing
struggle in scenarios where the robot encounters large amounts of debris. stairs.
Images of these wheels can be seen on the left and right.
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Presentation video
Hosted on YouTube. The player loads only when you press play.
Bill of materials
Show the remaining 2 pages
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RESCUE JUNIOR - Bill of Materials (BOM)
Team name: DELTA
Every Line/Maze team has to submit a bill of materials for their robot
Instructions:
*Enter Team name above
*All costs need to be in local currency and their approximate
conversion into dollars.
*All components worth less can be summarized in one line quantity 1
and their total cost (e.g. Screws)
*In the Local Currency Name column, the team must enter the name
of the local currency, if it is different from US dollars.
*In the software sheet, in the Author column, the name of the
designer, writer, or company that owns the software or library should
be entered. If the team created their own algorithm, dataset, or AI
model, they can include their names as authors.
*On the hardware sheet, in the "Author" column, enter the name of
the company, engineer, or part manufacturer. If the team made a
custom part, such as the robot housing, the team should include their
names as the authors.
*IMPORTANT* The "Hardware" sheet is not required for simulation
Team name: DELTA
# Component Part name Author Source
1 Drive Motors MG996R Servo 360° Rotation (Continuous) TowerPro makerstore.com.au
2 Long Range Distance Sensor Adafruit VL53L4CX Adafruit adafruit.com
3 Short Range Distance Sensor Adafruit VL53L4CD Adafruit adafruit.com
4 Small Servo Motors FS90MR 9g Servo Motor FeeTech core-electronics.com.au
5 Microcontroller Teensy 4.1 Teensy core-electronics.com.au
6 Cameras OpenMV AE3 OpenMV openmv.io
7 GY-BNO055 9 axis IMU No Brand aliexpress.com
8 Small Switch 125V 6A Switch E Switch digikey.com.au
9 Large Switch 125V 10A Switch E Switch digikey.com.au
10 Colour Sensor Adafruit AS7341 10 Channel Colour Sensor Adafruit adafruit.com
11 Battery Nano-Tech 2S Cell 7.4V Battery Turnigy power-systems Pre Owned/ Unknown
12 Silicone for wheels Transil 25A 2kg Silicone Barnes barnes.com.au
13 Ball Bearing Ball Bearing - 8 mm ID, 22 mm OD 7 mm Race Width RS Pro au.rs-online.com
14 Fasteners Pre Owned/ Unknown
15 Limit Switches LS2501F350C2A E-Switch digikey.com.au
16 Needle Roller Bearing 17 mm ID Roller Bearing Needle, 30 mm OD SKF au.rs-online.com
17 Robot Frame Custom Made Ethan Seymour CNC & 3D Printer
18 Wheels Custom Made Ethan Seymour 3D Printer & Silicone moulding
19 PCBs with electronic components Custom Made Ethan Seymour/ jlcpcb https://jlcpcb.com/
20 Cables Various Cables core-electronics.com.au
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Name of the Total Cost Total Cost ($)
Local Currency Local Currency U.S.A. Dollars
Australian Dollar
AUD $1,099.44 $774.45
Unit cost Total Cost Total Cost
Quantity (Local Unit cost (US Dollars) (Local (USA Dollars)
4 $16.70 $11.78 $66.80 $47.12
2 $21.21 $14.95 $42.42 $29.90
8 $21.21 $14.95 $169.68 $119.60
3 $5.70 $4.02 $17.10 $12.06
1 $57.15 $40.32 $57.15 $40.32
2 $141.88 $100.00 $283.76 $200.00
1 $12.59 $8.84 $12.59 $8.84
5 $3.26 $2.29 $16.30 $11.45
3 $0.93 $0.65 $2.79 $1.95
1 $26.98 $18.95 $26.98 $18.95
1 26.18 $18.45 $26.18 $18.45
1 $154.00 $108.59 $154.00 $108.59
1 $5.99 $4.22 $5.99 $4.22
1 $30.00 $20.96 $30.00 $20.96
4 $6.74 $4.75 $26.96 $19.00
2 $8.18 $5.77 $16.36 $11.54
1 21.24 $14.93 $21.24 $14.93
4 $1.00 $0.70 $4.00 $2.80
1 $104.14 $73.19 $104.14 $73.19
1 $15.00 $10.58 $15.00 $10.58
$0.00 $0.00
$0.00 $0.00
$0.00 $0.00
$0.00 $0.00
$0.00 $0.00
$0.00 $0.00
Team name: DELTA
# Name Software's Tool/LibraryDescription Source
1 Visual Studio Code Software's Tool Software code.visualstudio.com/
2 OpenMV IDE Software's Tool Camera Image Processing openmv.io
3 Fusion Software's Tool 3D CAD model & PCB Design autodesk.com
4 Github Software's Tool Code Version Control github.com
5 Bambu Studio Software's Tool 3D Printing Slicer bambulab.com
6 Makera Carvera Controller Software's Tool CNC Controller global.makera.com
7 Edge Impulse Software's Tool AI model tool www.edgeimpulse.com/
8 AS7341 Library Colour Sensor Library github.com/adafruit/Adafruit_AS7341
9 BNO005 Library IMU Library github.com/adafruit/Adafruit_BNO055
10 NeoPixel Library Neopixel RGB LED library github.com/adafruit/adafruit_neopixel
11 VL53L4CD Library Short Distance LRF library github.com/pololu/vl53l4cd-arduino
12 VL53L4CX Library Long Distance LRF library github.com/stm32duino/VL53L4CX
13 Servo Library Servo Motor Library github.com/michaelmargolis/SlowServo
14 BusIO Library I2C, UART, SPI control github.com/adafruit/Adafruit_BusIO
15 Sensor Library Sensor drivers github.com/adafruit/adafruit_sensor
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Name of the Total Cost Total Cost ($)
Local Currency Local Currency U.S.A. Dollars
Brazilian Real R$0.00 $0.00
Author Cost (Local Currency) Cost (USA Dollars)
Microsoft FREE FREE
OpenMV FREE FREE
Autodesk FREE/EDUCATION FREE/EDUCATION
Microsoft FREE FREE
Bambu Lab FREE FREE
Makera FREE FREE
Qualcomm Technologies FREE FREE
Adafruit FREE FREE
Adafruit FREE FREE
Adafruit FREE FREE
Pololu FREE FREE
STMicroelectronics FREE FREE
Michael Margolis FREE FREE
Adafruit FREE FREE
Adafruit FREE FREE
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Source code
The team's own source code, 34 KB. It is a download rather than part of this page, because a zip is something you open on your computer. It comes from GitHub, which some school networks block.