Comparison of the 4 Core Maze Search Algorithms : Which one is the best?
Comparison of the 4 Core Maze Search Algorithms : Which one is the best?
Before building physical hardware, robotics engineers and students simulate the micromouse problem in software. Simulation allows rapid iteration over algorithm designs, lets you benchmark performance across thousands of randomly generated mazes, and removes the cost and fragility of physical prototyping. The interactive simulation above lets you explore exactly this: pick an algorithm, generate a random maze, and watch how the mouse explores it step by step.
The Core Algorithms
The heart of any Micromouse solution is its maze-searching algorithm. Here are the four most commonly studied approaches.
Breadth-First Search (BFS) explores all cells at the current "distance" from the start before moving further out, layer by layer. It guarantees the shortest path in an unweighted maze. The trade-off is memory: it must track every cell in the current frontier simultaneously, which can be expensive in a large grid.
A* (A-star) with Manhattan heuristic is an informed search that combines BFS-style guarantees with a directional bias toward the goal. It estimates the remaining distance using the Manhattan distance — the number of horizontal and vertical steps between the current cell and the goal — and always expands the most promising cell first. A* typically visits significantly fewer cells than BFS while still guaranteeing optimality, making it a popular choice in software simulations.
How the Simulation Works
The simulation above generates a maze using a randomized depth-first carving algorithm. Starting from cell (0,0), it recursively visits neighbors in random order, removing walls between them until all cells have been visited. This guarantees a perfect maze — one where every cell is reachable and there is exactly one path between any two cells. Extra passages are then added at random to create multiple possible routes, making the pathfinding problem more interesting.
Once the maze is built, the selected search algorithm runs step by step. Each "visit" event moves the mouse to a new cell, colors it purple to indicate exploration, and updates the statistics panel. Orange cells show the current frontier — cells that have been discovered but not yet visited. When the goal is reached, the algorithm traces back through its parent pointers to reconstruct the final path, shown in amber.
Key statistics tracked in the simulation:
- Cells visited — total unique cells explored before reaching the goal. Lower means more efficient exploration.
- Steps taken — total movement steps, including revisits (important for DFS).
- Path length — length of the final optimal route from start to goal.
You can compare these numbers across algorithms by running the simulation multiple times on the same maze (using "Reset" without generating a new maze).
Source Reference : Web Simulator
Real-World Engineering Considerations
A physical micromouse must do far more than run a search algorithm. Real robots use infrared sensors to detect walls without touching them, rotary encoders to measure wheel travel with millimeter precision, and PID controllers to maintain straight-line movement even on battery sag. The onboard processor — often an ARM Cortex-M microcontroller — must run the maze-solving logic in under a millisecond per step while simultaneously managing motor PWM signals and sensor polling.
Competitive micromice typically run the maze twice. The first run is exploratory: the robot maps as much of the maze as possible using flood-fill. The second run is the speed run: the robot uses its complete map to race to the goal along the shortest known path, often hitting speeds of 1–2 meters per second through the tight 18 cm corridors.
Learning Outcomes
Working through micromouse simulation — whether in code or with a tool like the one above — teaches several transferable skills. Graph traversal and pathfinding are foundational concepts in game development (NPC navigation), logistics (route planning), and network design (packet routing). Understanding the trade-offs between DFS, BFS, A*, and flood-fill helps build intuition for when to choose each approach in real problems. And the constraint of an unknown environment pushes you to think about online algorithms — algorithms that make decisions with incomplete information.
Try It Yourself
Use the simulation above to run each of the four algorithms on the same 16×16 maze and record the "cells visited" number for each. You'll typically find that DFS explores the most cells, BFS and A* find optimal paths with A* being more focused, and flood-fill navigates efficiently but may revisit cells as it updates its distance map. Changing the maze size to 8×8 or 12×12 makes the differences even more apparent at a glance.
Micromouse is a beautifully self-contained engineering problem: tiny robot, fixed arena, one clear goal. But the algorithms powering it are the same ones that navigate self-driving cars, route internet traffic, and guide game characters through virtual worlds. Starting with a 16×16 grid is a surprisingly powerful entry point into all of them.
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