a simple solution to escape maze

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Left Wall Following Algorithm

The Left Wall Following Algorithm is a simple yet effective approach for maze navigation. While it does not guarantee the shortest path, it ensures a successful exit in almost 100% of test cases.

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Sensor-Based Approach

The robot makes decisions based on two key sensor readings:

• Front scan (-15° to 15°): Detects obstacles directly in front.
• Left side scan (45° to 135°): Measures the closest distance to the left wall.

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Algorithm Logic

Obstacle Detection

• If an obstacle is in front:
  • If the left-turn state is True, turn left.
  • Otherwise, turn right.

Path Navigation

• If no obstacle ahead:
  • If in the dead zone, turn right.
  • If between the dead line and keep line, move toward the keep line.
  • If between the keep line and boundary line, maintain position.
  • If outside the boundary line, move straight until a left wall is detected.

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Code Structure

class LeftWallFollower:
    def __init__(self):
        # Initialization logic here

    def clst_dtc_and_dir(self, start_degree, end_degree):
        # Detects the closest wall and its direction

    def scan_cb(self, msg):
        # Processes sensor scan data

    def follow_left_wall(self):
        # Implements velocity control and Bang-Bang Control logic

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Test Results

The algorithm was tested 10 times in different maze positions, yielding a 100% success rate:

• Perfect route: 60% of cases.
• Imperfect route: 40% of cases (still successfully escaped).

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Fault Tolerance

Two fault cases were observed:

1. Dead-end handling: If the robot turns left into a dead-end, it resets its state when encountering a new wall.
2. Exit room misinterpretation: The robot may bypass a small room at the exit due to sensor inaccuracy.

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Conclusion

The Left Wall Follower algorithm is an effective solution for autonomous robotic navigation. Future enhancements could include:

• PID-based control for smoother motion.
• SLAM integration for dynamic mapping.
• Machine learning enhancements for improved adaptability.

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