A Multi-Objective Modified PSO for Inverse Kinematics of a 5-DOF Robotic Arm
Source abstract
Abstract
In this paper, a new modified particle swarm optimization, m-PSO, is proposed, in which the novelty consists of proposing a fitness-based particle swarm optimization algorithm, PSO, which adapts the particles’ behavior rather than the PSO parameters and where particles evolve differently considering their level of optimality. A multi-objective optimization, MO, approach is then built based on m-PSO. In the proposed method, particles with fitness better than the mean local best are only updated toward the global best, while others keep moving in a classical manner. The proposed m-PSO and its multi-objective version MO-m-PSO are then employed to solve the inverse kinematics of a 5-DOF robotic arm which is 3D-printed for educational use. In the MO-m-PSO approach of inverse kinematics, the arm IK problem is formulated as a multi-objective problem searching for an appropriate solution that takes into consideration the end-effector position and orientation with a Pareto front strategy. The IK problem is addressed as the optimization of the end-effector position and orientation based on the forward kinematics model of the systems which is built using the Denavit–Hartenberg approach. Such an approach allows to avoid classical inverse kinematics solvers challenges such as singularities, which may simply harm the existence of an inverse expression. Experimental investigations included the capacity of the proposal to handle random single points in the workspace and also a circular path planning with a specific orientation. The comparative analysis showed that the mono-objective m-PSO is better than the classical PSO, the CSA, and SSA. The multi-objective variants returned accurate results, fair and better solutions compared to multi-objective variants of MO-PSO, MO-JAYA algorithm, and MO-CSA. Even if the proposed method were applied to solve the inverse kinematics of and educational robotics arms for a single point as well as for a geometric shape, it may be transposed to solve related industrial robotized arms withthe only condition of having their forward kinematics model.
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Research context
OpenAlex topics: Robotic Mechanisms and Dynamics · Metaheuristic Optimization Algorithms Research · Advanced Multi-Objective Optimization Algorithms
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Institute of Applied Science & Technologies of Sousse, University of Sousse, Sousse 4034, Tunisia
Research Group on Intelligent Machines, National Engineering School of Sfax, University of Sfax, Sfax 3029, Tunisia
College of Engineering and Technology, American University of Middle East, Egaila 54200, Kuwait
Centre for Artificial Intelligence Research and Optimisation, Torrens University, Adelaide 5000, Australia
Yonsei Frontier Lab, Yonsei University, Seoul 03722, Korea
References (39)
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