Inverse Kinematics Solution for Robot Based on Hybrid Optimization Algorithm

Inverse kinematics is one of the core issues in robot research, serving as the foundation for robot motion planning, trajectory generation and control. In this paper, a novel hybrid optimization algorithm is proposed to solve the inverse kinematics problem of robots effectively. It leverages the Quasi-Newton method to accelerate the convergence rate of the Electromagnetism-like algorithm (EM), and enhances its exploration capability for high-precision solutions. Calculating the decreasing direction of the objective function value and combining it with the random step size method for fast local search. Several experimental results indicate that the proposed method significantly improves solution accuracy while maintaining high search efficiency.