Robot Path Planning Using Genetic Algorithm

In the field of robotic path planning, the grid-based genetic algorithm is widely employed. This approach computes robot paths by discretizing the environment into grid cells, transforming the robot's movement into a constrained search problem. The genetic algorithm optimization method effectively resolves this search challenge through implementation mechanisms such as population initialization with feasible paths, fitness evaluation based on path length and obstacle avoidance, and genetic operators (crossover and mutation) for path refinement. Key algorithmic advantages include swarm intelligence for diverse solution exploration, global search capabilities to escape local optima, and adaptive parameter tuning for dynamic environments. Consequently, the grid-based genetic algorithm has become a reliable solution extensively applied in robotic navigation systems, with typical implementations involving grid encoding, path validation checks, and evolutionary iteration until convergence.