Genetic Algorithm for Path Planning

Using genetic algorithms for path planning is a widely adopted approach in robotics. Genetic algorithms are optimization techniques inspired by biological evolution processes, particularly suitable for solving complex path planning problems. The algorithm typically involves encoding potential paths as chromosomes, then applying genetic operators such as crossover (combining segments from parent paths), mutation (introducing random path modifications), and selection (prioritizing optimal paths based on fitness functions). Key implementation components include population initialization with feasible paths, fitness evaluation using distance or obstacle-avoidance metrics, and iterative generation evolution. This method effectively explores solution spaces to identify optimal navigation routes while avoiding obstacles, making genetic algorithms a robust and extensively implemented technique for autonomous robot path planning. Common MATLAB implementations might utilize functions like ga() from the Global Optimization Toolbox or custom-coded selection mechanisms.