MATLAB Source Code for Robot Path Planning Using Genetic Algorithm

Genetic algorithm-based robot path planning is an optimization problem that utilizes genetic algorithms to find optimal solutions. This algorithm simulates natural evolution processes through selection, crossover, and mutation operations to continuously improve solution quality. The MATLAB source code provides the main program framework, while supporting functions need to be implemented separately according to specific requirements. Key implementation aspects include: - Population initialization with feasible path representations - Fitness function design measuring path optimality (distance, smoothness, obstacle avoidance) - Genetic operators: tournament selection, single-point crossover, and mutation mechanisms - Termination criteria implementation (generation limit or convergence threshold) The code architecture allows customization through: - Addition of various constraints (obstacle avoidance, velocity limits) - Modification of fitness functions for different optimization objectives - Integration of different selection strategies and genetic operator parameters This implementation not only demonstrates genetic algorithm concepts but also provides practical experience in robotic path planning optimization. Users can extend functionality by implementing custom constraint handling, advanced genetic operators, or hybrid optimization approaches.