Solving Robotic Trajectory Planning Problems with Genetic Algorithms

This is a genetic algorithm implementation designed to solve robotic trajectory planning problems. Genetic algorithms are heuristic search algorithms that simulate natural evolution processes, employing genetic operations and fitness evaluation to find optimal solutions. In robotic trajectory planning, genetic algorithms can determine the optimal movement path for robots in given environments, enabling efficient trajectory planning and obstacle avoidance. The implementation typically involves: 1) Encoding robot paths as chromosomes, 2) Defining fitness functions based on path length, smoothness, and collision avoidance, 3) Applying selection, crossover, and mutation operators to evolve solutions. Key functions include population initialization, fitness calculation using Euclidean distance metrics, and genetic operators like tournament selection and uniform crossover. Through this genetic algorithm approach, robots can autonomously plan paths according to environmental constraints and target conditions, significantly improving overall navigation performance and efficiency.