Genetic Algorithm for Optimizing Point-to-Point Trajectory Planning in a 3-Link Robotic Arm

This code implements a Genetic Algorithm (GA) to optimize point-to-point trajectory planning for a 3-link robotic arm. The objective function of the proposed GA minimizes travel time and distance while ensuring the robot does not exceed predefined maximum torque limits and avoids collisions with obstacles in the workspace. To achieve this optimization, the following strategies are implemented: - Incorporation of additional variables and constraints to enhance problem complexity and precision, such as joint angle limits and obstacle avoidance conditions. - Introduction of supplementary optimization objectives, including minimizing energy consumption and maximizing motion smoothness through jerk reduction techniques. - Integration of advanced robotic dynamics considerations, such as inertial effects and friction models, using Euler-Lagrange equations for accurate torque calculations. - Systematic exploration of GA parameter configurations, including crossover and mutation rates, to improve solution quality through adaptive genetic operators. - Implementation of parallel computing and distributed optimization algorithms, utilizing MATLAB's Parallel Computing Toolbox to accelerate fitness evaluation and population processing. By implementing these strategies, the trajectory planning for the robotic arm is comprehensively optimized, resulting in enhanced performance and effectiveness. The GA workflow includes chromosome encoding of trajectory waypoints, fitness evaluation based on time-distance metrics and constraint penalties, and iterative refinement through selection, crossover, and mutation operations.