Chaotic Salp Swarm Algorithm for Inverse Kinematics of a 5-DOF Drill Floor Robot

As automation technology continues to advance in the global oil industry, intelligence is becoming a crucial trend. The focus of future research and development will be on reducing labor, minimizing operational risks, and enhancing work efficiency. The kinematics problem of drill floor robots is the basic problem for achieving intelligent operation, and inverse kinematics is the key problem for the kinematics solution. This paper focuses on the inverse kinematics solution of a drill floor robot that does not meet the Piper criteria. A modified Chaotic Salp Swarm Algorithm (MCSSA) is proposed for the robot. The algorithm utilizes Gaussian chaotic maps for population initialization and updates the salp leader positions, imitating the individual positions update mechanism of particle swarm optimization (PSO) to update the salp followers' positions. The improved method is used to calculate inverse kinematics solutions and is compared with classical PSO, Differential Evolution Algorithm (DE), Genetic Algorithm (GA), and Salp Swarm Algorithm (SSA) algorithms. The results show that the improved SSA algorithm performs better for inverse kinematics problems.