3-Month Maze Solving Robot Training Plan

Train students to design, program, and optimize autonomous maze solving robots using Arduino, sensors, and decision making algorithms.

Month 1: Maze Basics & Hardware Setup

Introduction to Maze Solving Robots

  • Types of mazes (open maze, perfect maze, multi-path maze)
  • Maze-solving techniques overview(Left-Hand Rule,
    Right-Hand Rule,Shortest Path Optimization)
  • Robot requirements: chassis, sensors, motor driver,micro controller

IR Sensor Interfacing and Calibration

  • Line detection basics with IR sensors
  • Threshold setting for black/white path detection
  • Track preparation: black line on white maze paths with junctions

Motor Driver & Chassis Assembly

  • Wiring L298N/L293D motor driver
  • Programming forward, left, right, stop actions
  • Real-time control via Serial Monitor

Basic Path Detection and Turning Logic

  • Detecting junctions and dead ends
  • Coding left-turn, right-turn, U-turn decisions based on sensor values
  • Visual debugging with Serial print

Month 2: Decision Making & Rule-Based Algorithms

Left-Hand and Right-Hand Rule Algorithms

  • Theory behind wall-following methods
  • Code implementation of Left-Hand Rule
  • Code implementation of Right-Hand Rule
  • Practical testing on a simple maze

Path Mapping with Multisensor Arrays

  • Integrating 3/5/8 IR sensor arrays
  • Detecting multiple path junction types (T, Y, Cross)
  • Logic table for complex path recognition

Memory-Based Path Recording

  • Storing turns and moves in an array
  • Recording junction decisions for replay or optimization
  • Display moves on Serial Monitor (optional OLED display)

Optimizing Movement Speed and Accuracy

  • Calibrating motor speed for sharp turns
  • Adjusting PID control (optional) for accurate line following within the maze
  • Practice runs on multi-path mazes

Month 3: Advanced Solving & Competition Track Challenges

Dead-End Detection and U-Turn Logic

  • Detecting dead ends and reverse logic
  • Managing alternate route decision-making
  • Handling loops and multi-choice paths

Fastest Path Relearning (Backtracking Concept)

  • Storing optimal path during the first maze run
  • Replay optimized shortest path on second run
  • Algorithm for retracing successful paths

Dynamic Maze Handling

  • Handling maze changes (blocked paths, opened exits)
  • Sensor recalibration mid-run
  • Error detection and fail-safe stops

Capstone Line Follower Robot Project

  • Final robot assembly and optimization
  • Full track run with PID, speed tuning, obstacle handling
  • Presentation and competition
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