β-FGJO: A General Metaheuristic Method for Inverse Kinematics Solution of Multi-DOF Robotic Manipulators

Robotic manipulators play a crucial role in providing support for automation and intelligence in various fields. The inverse kinematics problem becomes a significant challenge for modern robotic manipulator systems. This work proposes an enhanced Golden Jackal Optimization (
$\beta $
-FGJO) to solve the inverse kinematics problem in multi-degree-of-freedom (multi-DOF) robotic manipulators. In
$\beta $
-FGJO, the Fuch chaotic map is utilized to generate an efficient initial population to enhance search efficiency. Individual behavior is regulated by the adaptive
$\beta $
-distribution to improve both global exploration and local exploitation capabilities at different stages. Meanwhile, predators and prey in the population dynamically explore and exploit based on their energy level and hunger rate. Simulation results demonstrate that
$\beta $
-FGJO has shorter computation time, higher numerical precision and greater robustness. Compared to the best-performing method on PUMA560,
$\beta $
-FGJO improved time performance by 24.57%, while maintaining the same level of accuracy.