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Robots in 3D spaces with more than six degrees of freedom are redundant. Aredundant robot allows multiple configurations of the robot for the giventarget point in the dexterous workspace. The presence of multiple solutionshelps in resolving constraints in workspace such as object avoidance and energyminimization during trajectory planning. Inverse kinematics solutions of suchredundant robotics are intricate. The present study involves comparison ofdifferent metaheuristic optimization algorithms (MOA), which have a positionalerror, and identify a MOA for high precision of positioning of the end effectorof the robot. This study applies recent MOA for the inverse kinematics of hyperredundant nine degrees of freedom (DOF) robot arm by using forward kinematicsof the Denavit-Hartenberg (DH) parameters and compares the performance of thesealgorithms. The comparative study shows Bald Eagle Search (BES) algorithm hasbetter performance over other metaheuristic algorithms. BES algorithmoutperforms the other MOA in achieving the desired position with very highprecision and least positional error for a 9-DOF robot arm.

Bald Eagle Search Algorithm for High Precision Inverse Kinematics of Hyper-Redundant 9-DOF Robot