A-MAZE-ing Robot

🚀 Inspiration We were inspired by the challenge of creating an autonomous robot that can intelligently navigate a maze, detect paths, and make real-time decisions. Maze-solving robots are a great way to integrate computer vision, pathfinding algorithms, and sensor fusion, all while pushing the limits of Arduino’s capabilities.

🤖 What it does A-MAZE-ing Robot autonomously explores a maze using a combination of color detection, distance sensors, and motor control algorithms. It starts at an entry point, analyzes the maze layout, makes turns based on detected obstacles and colored markers, and finds an optimal route toward the goal. The robot adjusts dynamically based on the environment, ensuring efficient navigation without manual intervention.

🛠 How we built it Hardware Components:

Arduino UNO R4 Minima as the main microcontroller
JGA25-370 Gearmotors for movement
L298N Motor Driver to control motor speed and direction
SG90 Servo Motor (if used for additional movement mechanisms)
TCS230 Color Sensor for detecting colored pathways
Ultrasonic Sensor for obstacle detection and wall avoidance
Breadboard & Resistors for wiring and circuit connections
9V Battery + Snap Connector for power

Software & Algorithms:

PWM motor control for smooth navigation
Encoder-based odometry to estimate movement and turns
Color-based decision making to recognize and respond to different maze markings
Pathfinding logic to decide left/right turns dynamically

🔧 Challenges we ran into Hardware assembly issues – Some components didn’t fit well on the chassis, requiring modifications and reassembly. Fine-tuning the color sensor – The TCS230 color sensor was highly sensitive to lighting conditions, which made color detection inconsistent. We had to calibrate it carefully and adjust thresholds to improve accuracy. Figuring out pathfinding methods – Choosing the best navigation algorithm was a challenge. We experimented with different approaches like wall-following, flood-fill, and left-hand rule before refining our logic for better efficiency.

🎉 Accomplishments that we're proud of Successfully integrating multiple sensors to make real-time decisions. Overcoming sensor calibration difficulties to improve color recognition. Implementing an encoder-based distance calculation to improve movement accuracy. Making our robot autonomously reach its goal despite obstacles and dead ends.

📚 What we learned The importance of hardware-software integration – Small inaccuracies in sensor readings can cause major errors in navigation. How to optimize sensor thresholds for different lighting and environmental conditions. The value of modular programming – Breaking down control logic into reusable functions made debugging much easier. How motor encoders and odometry can improve movement precision in robotics.

🔮 What's next for A-MAZE-ing Robot? Improved pathfinding algorithms – Implementing A* or Dijkstra’s algorithm for more efficient maze navigation. Better localization – Using additional IMU sensors for more accurate heading estimation. Mapping capabilities – Incorporating Simultaneous Localization and Mapping (SLAM) to visualize the maze in real time. Competing in robotics challenges – Taking our robot to competitions where autonomous navigation is key!