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

Abstract

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.