A hybrid swarm intelligence of artificial immune system tuned with Taguchi-genetic algorithm and its field-programmable gate array realization to optimal inverse kinematics for an articulated industrial robotic manipulator

作者：Huang Hsu Chih^*; Xu Sendren Sheng Dong; Wu Chang Han

来源：Advances in Mechanical Engineering, 2016, 8(1): 1687814015626380.

DOI：10.1177/1687814015626380

• Artificial immune system
• inverse kinematics
• Taguchi
• system-on-a-programmable-chip

This article presents a hybrid swarm intelligence of artificial immune system tuned with Taguchi-genetic algorithm and its field-programmable gate array realization to optimal inverse kinematics for a 5-degree-of-freedom industrial robotic manipulator using system-on-a-programmable-chip technology. This hybridization strategy with a Taguchi-genetic algorithm parameter tuner improves the performance in conventional artificial immune system paradigms. The field-programmable gate array realization of the proposed artificial immune system tuned with Taguchi-genetic algorithm is more effective in practice for real-world embedded applications. This system-on-a-programmable-chip-based artificial immune system tuned with Taguchi-genetic algorithm is then applied to the optimal inverse kinematics redundancy solver of a 5-degree-of-freedom robotic manipulator. The optimal joint configuration is obtained by minimizing the pre-defined affinity function in artificial immune system tuned with Taguchi-genetic algorithm for real-world embedded robotics application. The experimental results and comparative works are presented to show the effectiveness and merit of the proposed system-on-a-programmable-chip-based artificial immune system tuned with Taguchi-genetic algorithm intelligent inverse kinematics redundancy solver for a 5-degree-of-freedom industrial robotic manipulator. This system-on-a-programmable-chip-based artificial immune system tuned with Taguchi-genetic algorithm solver outperforms the conventional solvers, such as geometric solvers, Jacobian-based solvers, hybrid genetic algorithm solvers, and particle swarm optimization solvers.

• 出版日期2016-1