Genetic Algorithm for Robotic Path Planning with MATLAB Implementation

Genetic Algorithm is a computational method based on principles of natural selection and genetics. In robotic path planning, genetic algorithms can be employed to find the shortest possible path, enabling robots to reach their destination efficiently. The MATLAB implementation typically involves key components such as: population initialization with random path solutions, fitness evaluation using path length calculations, selection operators (e.g., tournament selection), crossover operations for path recombination, and mutation operators to maintain diversity. The code structure includes functions for path encoding/decoding, obstacle avoidance constraints handling, and convergence monitoring. Using MATLAB for genetic algorithm development facilitates straightforward testing and debugging, allowing continuous optimization and improvement of robotic path planning performance. This approach represents an efficient and reliable methodology that significantly enhances both the precision and efficiency of robot navigation systems.