This content originally appeared on DEV Community and was authored by Jeff Pang Liu
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.
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.
Algorithm Logic
Obstacle Detection
- If an obstacle is in front:
- If the left-turn state is
True, turn left. - Otherwise, turn right.
- If the left-turn state is
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.
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
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).
Fault Tolerance
Two fault cases were observed:
- Dead-end handling: If the robot turns left into a dead-end, it resets its state when encountering a new wall.
- Exit room misinterpretation: The robot may bypass a small room at the exit due to sensor inaccuracy.
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.
This content originally appeared on DEV Community and was authored by Jeff Pang Liu
Jeff Pang Liu | Sciencx (2025-02-07T04:05:49+00:00) a simple solution to escape maze. Retrieved from https://www.scien.cx/2025/02/07/a-simple-solution-to-escape-maze/
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