NeuroMaze - AI Hybrid MicroMouse

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  NeuroMaze simulation - 16x16 maze solved using Flood-Fill + BFS in 14 seconds. Green path shows optimal route to center.

In## Inspiration

In Alliance University's Micromouse challenge, we saw that hardware is too expensive for many Indian students. A full Micromouse robot costs ₹15k+. We wanted to build a hybrid solution that works in simulation first, so any student with a laptop can participate and learn.

What it does

NeuroMaze solves any 16x16 maze autonomously:

• Phase 1: Explores unknown maze and maps walls using IR sensor logic
• Phase 2: Uses Flood-Fill algorithm to calculate distance from every cell to center
• Phase 3: Uses BFS to find and sprint on the shortest path
• Real-time visualization in Python Pygame
• Same logic can run on ESP32 + IR sensors for real hardware

It reaches the center in under 14 seconds in simulation.

How we built it

• Algorithm: Flood-Fill + BFS + DFS exploration
• Stack: Python 3.9, Pygame for visualization, Gazebo-compatible logic
• Hardware Ready: Code optimized for ESP32-S3 (<50KB), supports SFH4545 IR sensors, MPU6500 gyro
• We started with a simple wall-follower, then upgradedspiration

What it does

How we built it

Challenges we ran into

Accomplishments that we're proud of

What we learned

What's next for NeuroMaze - AI Hybrid MicroMouse

Built With

• algorithm
• bfs
• esp32
• flood-fill
• gazebo
• iot
• pygame
• python