Team Mazecraze-MICROMOUSE
Robofest 5.0
MazeCraze is a micromouse platform featuring a unique integrated PCB-chassis architecture. Engineered for the Gujarat Robofest Prototype Round, the bot utilizes an ESP32-WROOM core to execute a A (A-Star) search algorithm, ensuring mathematically optimal pathfinding (f(n) = g(n) + h(n)).
We are proud to share the story of Team MazeCraze from Jadavpur University. Led by Prosmit Das along with Prakriti Ghosh, Tanistha Saha, and Shreyan Dey, our team navigated the Gujarat Robofest from initial qualification to the prestigious prototype round.
Our project, an autonomous maze-solving bot, represents a patented innovation in integrated mechanical and electronic design.
The Engineering Challenge: PCB vs. Veroboard
While our core innovation features a sleek design where the PCB itself serves as the chassis, the prototype round pushed us to demonstrate our "hardware-from-scratch" capabilities. We opted for the grueling task of hand-soldering the entire system onto a Veroboard. This required hours of meticulous wiring to connect:
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The Brain: An ESP32 WROOM, chosen for its dual-core 240 MHz speed and rich peripheral set.
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The Movement: Two N20 micro-gear motors (12V, 100rpm) driven by the TB6612FNG dual motor driver.
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The Vision: Six VL6180X Time-of-Flight (TOF) sensors for millimeter-accurate wall detection.
The Prototype Round Hurdle
In the high-stakes environment of the prototype round, we encountered the "soldering gremlins" that every engineer fears. Despite our best efforts, a multimeter check revealed a critical failure: zero power output from the TB6612FNG motor driver.
The TB6612FNG is sensitive to thermal limits and requires precise decoupling. A single cold solder joint or a microscopic bridge in the dense Veroboard wiring was enough to stall our motors. While it was a difficult moment on the competition floor, it provided a masterclass in troubleshooting and the importance of robust hardware validation.
Our Intelligent Edge: The A* Algorithm
Our bot utilizes the A (A-Star) Algorithm*, which uses a cost function ($f(n) = g(n) + h(n)$) to find the shortest path with mathematical certainty.
By combining backtracking for mapping and A* for the final sprint, we created a system that isn't just a "runner," but a truly intelligent navigator.
Securing a patent and reaching the prototype round of Robofest 2025 has been a journey of immense learning.