Inverse kinematics of 7-DOF Redundant Manipulator Based on Improved Dung Beetle Optimization Algorithm

Abstract

Since the redundant manipulator can move freely and adapt well to its surroundings, one of the primary issues in robotics is its inverse kinematics. An improved Dung Beetle Optimization (I-DBO) algorithm is proposed, and the scene fitness function of the improved DBO algorithm is established. Tent chaotic mapping is used to generate extremely diverse initial population in the population initialization step, which helps to overcome the local optimization problem in the traditional DBO technique and improve the diversity of initial population solutions. With the addition of the improved sinusoidal algorithm, the global exploration and local exploitation abilities of dung beetles were enhanced, and the likelihood of falling into the local optimal was decreased. Additionally, the global search ability was improved by expanding the scope of the initial population solution space search. Tests and comparative analyses demonstrate that the enhanced DBO algorithm resolves the local convergence issue, enhances stability and convergence precision, and demonstrates the new method’s logic. The inverse kinematics of redundant robotic arms can be solved with great significance by using this method, which also enhances the global exploration and local development ability of dung beetles and avoids the local optimal solution of the DBO algorithm.