Solution of Inverse Kinematics for Industry Robot Based on Multi-Strategy Improved Chimpanzee Optimization Algorithm

Abstract

During the process of inverse kinematics solving for a robotic arm, heuristic optimization algorithms have been widely applied. However, due to the complex structure of the robotic arm, these algorithms suffer from drawbacks such as slow convergence speed and a tendency to get trapped in local optima. Therefore, a novel metaheuristic optimization algorithm called the Chimpan-zee Optimization Algorithm (ChOA) has been introduced, along with multiple strategy improvements to enhance its global search capability and convergence optimization performance. Through simulation experiments on the modified Chim-panzee Optimization Algorithm (MChOA), it has been demonstrated that this method outperforms the traditional ChOA algo-rithm in terms of convergence speed and accuracy, effectively addressing the issues of slow convergence and being trapped in local op-tima.