Optimization of manipulator inverse kinematics using improved particle swarm algorithm for enhanced manufacturing efficiency

In the context of digital manufacturing and intelligent manufacturing systems, optimizing the inverse kinematics (IK) of manipulators is crucial for improving manufacturing efficiency and productivity. The authors propose an improved particle swarm optimizer (IPSO) with adaptive inertia weight and asynchronous learning factors to enhance the optimization performance. The proposed algorithm introduces a novel mutation strategy where selected initial particles undergo mutation to generate new particles, thereby increasing swarm diversity. The best-performing particles after mutation replace the initial ones to improve global optimization capability. The effectiveness of the IPSO algorithm is evaluated through thirteen classical benchmark functions and compared with genetic algorithms (GA), standard particle swarm optimization (PSO), linear decreasing weight PSO (LDWPSO), and biogeography-based PSO (BLPSO), quantum-behaved particle swarm optimization (QPSO), Levy Flight quantum-behaved particle swarm optimization (LQPSO), Gaussian quantum-behaved particle swarm optimization approaches (GQPSO). Experimental mean results demonstrate that the IPSO algorithm outperforms these algorithms. The IPSO algorithm and the other algorithms subsequently were employed to estimate the performance for tackling the IK problem of a manipulator and the comparison results demonstrate the superior performance of IPSO algorithm in terms of accuracy and convergence rate. Finally, the practical efficacy of the IPSO algorithm was validated through experimental trials conducted on a custom-designed six-degree-of-freedom (6-DOF) collaborative robotic manipulator platform, which demonstrated the viability of IPSO algorithm in real-world manufacturing applications. The findings suggest that the proposed IPSO algorithm offers a promising solution for enhancing manipulator control efficiency in digital manufacturing systems. The IPSO code is available at: https://10.6084/m9.figshare.27753549 .