2025 Regional Competition Robot

Competition Challenge

The 2025 Regional Challenge requires robots to:

1. Navigate an obstacle course autonomously
2. Identify colored objects using computer vision
3. Pick and place objects into designated zones
4. Complete tasks within a 3-minute time limit

Our Approach

We’re building a modular robot with three key subsystems:

• Navigation: Autonomous path planning with ultrasonic sensors
• Vision: OpenCV-based object detection and tracking
• Manipulation: 3-DOF robotic arm for picking and placing

Current Progress

✅ Completed

• CAD design of chassis and arm
• 3D printed chassis components
• Basic navigation system working
• Motor driver integration
• Power management system

🚧 In Progress

• Computer vision object detection (80% complete)
• Gripper mechanism refinement
• Autonomous routine optimization
• Testing and calibration

📋 To Do

• Emergency stop system
• Competition field testing
• Driver practice and backup modes
• Final paint and branding

Technical Highlights

Computer Vision

We’re using OpenCV with HSV color space filtering to detect objects:

import cv2
import numpy as np

def detect_colored_object(frame, color_range):
    """
    Detect objects of a specific color in the frame
    Returns the centroid of the largest detected object
    """
    hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)
    mask = cv2.inRange(hsv, color_range['lower'], color_range['upper'])

    # Find contours
    contours, _ = cv2.findContours(mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)

    if contours:
        # Get largest contour
        largest = max(contours, key=cv2.contourArea)
        M = cv2.moments(largest)

        if M["m00"] > 0:
            cx = int(M["m10"] / M["m00"])
            cy = int(M["m01"] / M["m00"])
            return (cx, cy)

    return None

# Example usage
red_range = {
    'lower': np.array([0, 100, 100]),
    'upper': np.array([10, 255, 255])
}

cap = cv2.VideoCapture(0)
while True:
    ret, frame = cap.read()
    position = detect_colored_object(frame, red_range)

    if position:
        cv2.circle(frame, position, 10, (0, 255, 0), -1)

    cv2.imshow('Detection', frame)
    if cv2.waitKey(1) & 0xFF == ord('q'):
        break

cap.release()
cv2.destroyAllWindows()

Path Planning

Our navigation system uses a simple but effective algorithm:

1. Scan environment with ultrasonic sensors
2. Create occupancy grid of obstacles
3. Use A* pathfinding to plan route
4. Execute movement with PID-controlled motors

Design Decisions

Why Raspberry Pi over Arduino?

• Need processing power for computer vision
• Python is more suitable for complex algorithms
• Can run OpenCV natively
• Arduino handles low-level motor control via serial

Modular Architecture

Each subsystem operates independently with defined interfaces:

• Navigation Module: Handles movement and obstacle avoidance
• Vision Module: Detects and tracks objects
• Arm Module: Controls gripper and arm positioning
• Main Controller: Coordinates all modules

This allows parallel development and easier debugging.

Timeline

• Week 1-2: Design and CAD modeling ✅
• Week 3-4: Chassis build and motor integration ✅
• Week 5-6: Vision system development 🚧
• Week 7-8: Arm mechanism and gripper
• Week 9-10: Integration testing
• Week 11: Competition field practice
• Week 12: Competition day! 🏆

Competition Day: November 15, 2025

We’re excited and nervous! Follow our progress on this website and our social media.

Team Roles

• Sarah Martinez: Team lead, vision system
• Alex Chen: Mechanical design, CAD
• Taylor Johnson: Software architecture, navigation
• Jordan Kim: Arm control, gripper mechanism

Support Our Team

Want to help? We’re looking for:

• Sponsorship for parts and competition fees (Goal reached! Thank you!)
• Mentors with robotics or computer vision experience
• Practice space with good lighting for vision testing

Contact us at robotics@school.edu

Last updated: October 17, 2025 Competition in 29 days!