MATLAB Source Code for Robot Path Planning Using Genetic Algorithm

This document provides a comprehensive explanation of the MATLAB source code for robot path planning utilizing genetic algorithms. We will systematically break down the algorithm implementation details, including key components such as population initialization, fitness function calculation based on path length and obstacle avoidance, selection operations using roulette wheel or tournament methods, crossover techniques for path recombination, and mutation operators for solution diversity. The discussion will include practical code examples demonstrating chromosome encoding for path representation, obstacle detection mechanisms, and convergence criteria implementation. We will analyze the algorithm's advantages in handling complex environments and global optimization capabilities, while addressing limitations like premature convergence and computational complexity. The document concludes with suggestions for improvements such as adaptive parameter tuning, hybrid approaches combining local search techniques, and potential extensions for multi-objective optimization or dynamic environments. Through this material, readers will gain thorough understanding of genetic algorithm-based path planning and be able to effectively implement this approach in their robotics projects.