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

TL;DR

This paper compares various metaheuristic algorithms for solving inverse kinematics of a hyper-redundant 9-DOF robot, finding that the Bald Eagle Search algorithm achieves superior precision and lowest positional error.

Contribution

The study introduces the application of the Bald Eagle Search algorithm for high-precision inverse kinematics in hyper-redundant robots, outperforming other metaheuristics.

Findings

BES algorithm achieves the highest accuracy in end-effector positioning.

BES outperforms other metaheuristics in minimizing positional error.

The study demonstrates BES's effectiveness for complex 9-DOF robot inverse kinematics.

Abstract

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.