This repository contains the documentation for Chabots participation in the WRO Future Engineers 2024 category. Our robot was designed and built by a team of Mexican students passionate about robotics and education.
- The Team
- The Challenge
- Robot Overview
- Mobility Management
- Power and Sense Management
- Code Overview
- Obstacle Management
- Construction Guide
- Cost Report
- Resources
- License
Age: 22
Role: Software Development
[Short bio of the teammate – use placeholder text if teammate is unknown]
Age: 20
Role: Mechanical Design
[Short bio of the teammate – use placeholder text if teammate is unknown]
Age: 20
Role: Captain, Electronics & Software Design
I am a self-taught robotics enthusiast with experience in embedded systems, software, and mechanical integration. my team ChaBots Ocelot won Mexico Robocup soccer Open second place and achieved multiple national awards in programming and robotics.
"I enjoy setting nearly impossible goals to push myself while learning. I believe that learning should always lead to building something real."
[Short bio of the teammate – use placeholder text if teammate is unknown]
Age: 22
Role: Coach
I've been involved in robotics for 14+ years being a programmer for most of the projects I've taken part in. I've had may regional, national and international experiences. Now I'm working in sharing my knowledge with more people to push further their level and potential as well as helping them achieve their goals and find their passion.
"I like to face challenges and even more so when it's with more people. Learning and creating something is better when shared."
The WRO Future Engineers challenge pushes students to create fully autonomous self-driving vehicles. Each robot must:
- Navigate a dynamically randomized track
- Detect and avoid colored obstacles (green/red blocks)
- Execute a parallel parking maneuver
Scoring is based on:
✔ Performance on track
✔ Obstacle handling
✔ Documentation quality
✔ Innovation and engineering rigor
Read more: WRO Official Site
Name: [To be completed]
Meaning / Acronym: [Optional]
| Front | Back |
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| Left | Right |
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| Top | Bottom |
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Design Notes:
We opted for custom-built steel axles with a diameter of 4mm, connected to Pololu 25D 6V HP motors, which provided sufficient power and torque to meet our performancer requireeriments. The motors have a maximum RPM of, max spee 480, but theour torque output was initially too high for optimal speed. To address this, we ilemented ado we decide use an extra reductional to 2:1 gear reduction, effectively doubling the speed while maintainingto have double speed and approximately 1.8 kg·cm of/ torque—suitable for our application.
The drivetrain base and gear assemblys were manufactured in-hby ourse. We used PLA Carbon Fiber filameona QIDI Q1 proprinter for extra strength and durability. We designed and printed d, and PLA Couble Helical Gears tld energy transmission efficiency and reduce mechanical wear.
The steel axeis wer to size manually and shapemade by our self using an steel rod, we cut it at the needed size, and usinged a Ddremel tool to create D-shaped shafts, which ensured a secure grip with the wheel hubs. Our wheels were also custom-built using 3D-printed make D Shaft to ensure grip from our wheels, our wheels were also made using , using the same qidi, the made the tire rims, and motor shaft couplers sourcedused some motor coupler shaft we bugth from AaliEexpress. For tires, we repurposed LEGO rubber tires after determining that fabricating our own rubber tires was not viable.
Motor: Pololu 25D 6V HP
Gear Ratio: 20.4:1
Max RPM: 480
Planned Improvements for National Phase:
- Upgrade to Maxon DCX19 motors for better power-to-weight ratio.
- Implement a differential gear system for smoother cornering.
- Replace LEGO tires with higher-grip, custom-molded polyurethane ones., we used lego wheels tires as we realize that was to dificult to make our selfs tires, d us a remel to rim and rom liess
Motor:
Gear Ratio: [Insert ratio]
Max RPM: [Insert speed]
Potential Improvements:
- Switch to brushless motors for better efficiency and thermal performance.
- Redesign wheel hubs to allowfor quicker swapping and maintenance.
- Manufacture custom
- Add diferential gear
- Make our self tires using cast polyiurethane for improved traction.
We build an ackermann stering systedm to ensure smooth and better turns, we used a mg 995 servo motor cause was dificult to get a better one justo for now, all the mechanism is mounted in our 3d printed chasis and we use PolyMax PC to ensure that our mehcanism have enough sttength, Fiberon PC
Servo Model: MG995[Insert model]
Rotation Range: 0-180
Future Upgrades:
- Improve servo angle feedback
- Add software-based correction via IMU
The main control loop runs on the Teensy 4.0, handling:
Core Features:
- Real-time motor control with PID feedback
- State machine for autonomous navigation modes
- Sensor data fusion from IMU and OTOS
- UART communication protocol with vision systems
Handles primary computer vision tasks:
Features:
- Real-time color blob detection for red/green cubes
- Line detection and tracking algorithms
- Centroid calculation for object following
- Adaptive thresholding for varying lighting conditions
Our LIDAR system provides 360° environmental awareness with sector-based analysis:
class LidarSectorAnalyzer:
def __init__(self):
self.target_angles = [0, 90, 180, 270] # Cardinal directions
self.angle_tolerance = 5 # ±5° sector width
self.sector_data = {angle: [] for angle in self.target_angles}Key Features:
- Real-time distance measurements at cardinal directions (0°, 90°, 180°, 270°)
- Statistical analysis with moving averages for noise reduction
- Quality filtering to exclude unreliable readings
- Continuous monitoring with configurable reporting intervals
Applications:
- Wall detection for parallel parking
- Obstacle distance verification
- Navigation corridor analysis
- Backup sensor for vision system failures
The Optical Tracking Odometry Sensor provides precise position and heading data:
def runExample():
myOtos = qwiic_otos.QwiicOTOS()
myOtos.begin()
myOtos.calibrateImu()
myOtos.resetTracking()
while True:
myPosition = myOtos.getPosition()
# Returns X, Y coordinates in inches and heading in degreesCapabilities:
- Sub-millimeter position accuracy
- Real-time heading calculation
- IMU calibration for drift compensation
- Continuous tracking with 0.5s update rate
Advanced color detection using PiCamera2 for improved reliability:
Features:
- HSV color space processing for better color separation
- Morphological operations for noise reduction
- Multi-threshold detection for red color (handles hue wraparound)
- Real-time FPS monitoring and performance optimization
- Automatic image capture for debugging
Color Ranges:
- Blue cubes: HSV(100-130, 80-255, 80-255)
- Red cubes: HSV(0-10, 80-255, 80-255) + HSV(170-180, 80-255, 80-255)
-
Sensor Acquisition Layer
- LIDAR: 360° distance data at 10Hz
- OTOS: Position/heading at 2Hz
- Camera: Color blobs at 30Hz
- IMU: Orientation at 100Hz
-
Processing Layer
- Sensor fusion algorithms
- Computer vision processing
- Statistical filtering
- State estimation
-
Control Layer
- PID motor control
- Path planning algorithms
- Decision state machine
- Safety monitoring
-
Hardware Interface Layer
- Motor driver commands
- Servo positioning
- LED indicators
- Emergency stop
/code/
├── arduino/
│ ├── main.cpp # Main control loop
├── vision/
│ ├── main.py # Main vision script
├── raspberry_pi/
│ ├── lidar_analyzer.py # LIDAR sector analysis
│ ├── otos_reader.py # Position tracking
- Vision Processing: 30 FPS color detection
- Control Loop: 100 Hz motor control updates
- LIDAR Refresh: 10 Hz environmental scanning
- Position Update: 2 Hz absolute positioning
- Communication Latency: <10ms between subsystems
- Debugging: Real-time data logging and visualization
- Calibration: Automated sensor calibration routines
- Testing: Unit tests for critical algorithms
- Simulation: Virtual environment for algorithm development
The robot detects and reacts to obstacles in real-time using multiple sensor modalities:
- Primary: Enhanced color detection via PiCamera2 system
- Verification: LIDAR distance measurements for obstacle confirmation and navigation
- Backup: OTOS position tracking for navigation consistency
- Dynamic turning decision system based on cube color and position
- Follow-the-object mode with PID steering based on cube centroid
- Multi-sensor verification to reduce false positives
- Adaptive speed control based on obstacle proximity
- in construcction
STL Files Folder: 3d-models/
Sections to complete:
- Step 0: 3D printing
- Step 1: Steering system
- Step 2: Powertrain and motor mount
- Step 3: Electronic layout
- Step 4: Wiring
- Step 5: Upload firmware
| Item | Qty | Unit Cost (MXN) | Total (MXN) |
|---|---|---|---|
| Teensy 4.0 | 1 | 800 | 800 |
| Raspberry pi camera v2 | 1 | 400 | 400 |
| 2.2Ah LiPo 11.1V Battery | 1 | 600 | 600 |
| Gearmotor 50:1 Pololu | 1 | 400 | 400 |
| MG90S Micro Servo | 1 | 90 | 90 |
| SparkFun OTOS | 1 | 2400 | 2400 |
| VNH5019 | 1 | 800 | 800 |
| PLA Filament (prototypes) | - | 1kg = 350 | 350 |
| PLA-CF Filament (finals) | - | 200g = 150 | 150 |
| Raspberry pi 5 | 1 | 2800 | 2800 |
| RPlidar C1 | 1 | 2500 | 2500 |
| Total | 10290 MXN |
- Chabots Main Site
- WRO Future Engineers Rules PDF
- GitHub Repos (to be added)
MIT License
Permission is hereby granted, free of charge, to any person obtaining a copy of this software...
(Full license text here)
Document maintained by Chabots | Last updated: June 2025