  Robots in 3D spaces with more than six degrees of freedom are redundant. A
redundant robot allows multiple configurations of the robot for the given
target point in the dexterous workspace. The presence of multiple solutions
helps in resolving constraints in workspace such as object avoidance and energy
minimization during trajectory planning. Inverse kinematics solutions of such
redundant robotics are intricate. The present study involves comparison of
different metaheuristic optimization algorithms (MOA), which have a positional
error, and identify a MOA for high precision of positioning of the end effector
of the robot. This study applies recent MOA for the inverse kinematics of hyper
redundant nine degrees of freedom (DOF) robot arm by using forward kinematics
of the Denavit-Hartenberg (DH) parameters and compares the performance of these
algorithms. The comparative study shows Bald Eagle Search (BES) algorithm has
better performance over other metaheuristic algorithms. BES algorithm
outperforms the other MOA in achieving the desired position with very high
precision and least positional error for a 9-DOF robot arm.
