Inverse Kinematics Solution of 6-DOF Manipulator Based on Multi-Objective Full-Parameter Optimization PSO Algorithm.

Area of Science:

• Robotics
• Computational Intelligence
• Optimization Algorithms

Background:

• Existing inverse kinematics (IK) algorithms for manipulators suffer from low accuracy, efficiency, and instability.
• Particle Swarm Optimization (PSO) has limitations in addressing complex IK problems.

Purpose of the Study:

• To propose a novel multi-objective full-parameter optimization particle swarm optimization (MOFOPSO) algorithm.
• To enhance the accuracy, efficiency, and stability of IK solutions for 6-DOF manipulators.

Main Methods:

• Developed a MOFOPSO algorithm incorporating position, posture, and joint factors into the multi-objective function.
• Improved PSO's global and local search abilities by analyzing influencing factors.
• Implemented a localized uniform distribution method for the initial population.
• Introduced an iteration factor to design inertia weight, asynchronous learning factor, and time factor.

Main Results:

• MOFOPSO demonstrated superior performance in solving IK problems for a 6-DOF manipulator compared to six other algorithms.
• The proposed method achieved high accuracy and efficiency in IK solutions.
• Ensured the stability of the manipulator during the IK solving process.

Conclusions:

• The MOFOPSO algorithm offers a robust and effective solution for manipulator inverse kinematics.
• This approach significantly advances the state-of-the-art in robotic motion planning and control.
• The findings are crucial for developing more precise and reliable robotic systems.