Game-Based Social Learning Particle Swarm Optimizer for Inverse Kinematics of Robotic Arms

TY - JOUR

T1 - Game-Based Social Learning Particle Swarm Optimizer for Inverse Kinematics of Robotic Arms

AU - Hu, Kexin

AU - Ma, Zhongjing

AU - Zou, Suli

AU - Li, Jian

AU - Zhang, Jinhui

N1 - Publisher Copyright: © 2016 IEEE.

PY - 2025

Y1 - 2025

N2 - Robot inverse kinematics is the foundation of robotics and an indispensable part of robot development and applications. The inverse kinematics of a robotic arm involves its specific configuration, the non-convex coupling relationships between joints, and the presence of multiple solutions, among other factors. Existing works usually focus on the robotic arm as a whole, emphasizing the pose accuracy of the robot's end-effector, without considering the individuality of each joint. In this letter, the inverse kinematics problem of robotic arms is formulated as a non-convex optimization problem, with the goal of optimizing the contribution of each joint. Game theory is rigorously introduced to convert the optimization problem into a joint game Nash equilibrium (NE) problem. A generalized Lagrange method is used to handle coupling constraints by introducing additional penalty terms into the objective function. We propose a game-based social learning particle swarm optimization (GSLPSO) algorithm that combines NE strategy and social learning mechanism to enhance the problem-solving process. The proposed GSLPSO is compared with existing relevant algorithms and verified on a real-world robotic arm AUBO i16, which proves the superiority and practicality of the proposed algorithm.

AB - Robot inverse kinematics is the foundation of robotics and an indispensable part of robot development and applications. The inverse kinematics of a robotic arm involves its specific configuration, the non-convex coupling relationships between joints, and the presence of multiple solutions, among other factors. Existing works usually focus on the robotic arm as a whole, emphasizing the pose accuracy of the robot's end-effector, without considering the individuality of each joint. In this letter, the inverse kinematics problem of robotic arms is formulated as a non-convex optimization problem, with the goal of optimizing the contribution of each joint. Game theory is rigorously introduced to convert the optimization problem into a joint game Nash equilibrium (NE) problem. A generalized Lagrange method is used to handle coupling constraints by introducing additional penalty terms into the objective function. We propose a game-based social learning particle swarm optimization (GSLPSO) algorithm that combines NE strategy and social learning mechanism to enhance the problem-solving process. The proposed GSLPSO is compared with existing relevant algorithms and verified on a real-world robotic arm AUBO i16, which proves the superiority and practicality of the proposed algorithm.

KW - Al-based methods

KW - Kinematics

KW - game theory

KW - optimization

UR - https://www.scopus.com/pages/publications/105006580030

U2 - 10.1109/LRA.2025.3573627

DO - 10.1109/LRA.2025.3573627

M3 - Article

AN - SCOPUS:105006580030

SN - 2377-3766

VL - 10

SP - 7078

EP - 7085

JO - IEEE Robotics and Automation Letters

JF - IEEE Robotics and Automation Letters

IS - 7

ER -