Improved Bald Eagle Search Optimization Algorithm for the Inverse Kinematics of Robotic Manipulators.

Area of Science:

• Robotics
• Computational Intelligence
• Optimization Algorithms

Background:

• Robotic manipulator inverse kinematics is complex due to nonlinear and coupled dynamics.
• Existing optimization algorithms often struggle with local optima and efficiency in these problems.

Purpose of the Study:

• To introduce an enhanced Bald Eagle Search (BES) algorithm for solving robotic manipulator inverse kinematics.
• To improve the global search efficiency and convergence speed of the BES algorithm by incorporating Lévy flight strategy.

Main Methods:

• The study proposes an improved Bald Eagle Search (BES) algorithm, integrating Lévy flight for enhanced global exploration.
• The algorithm's performance was evaluated on robotic manipulator inverse kinematics problems and benchmark CEC2017 test functions.
• Comparative analysis was conducted against other optimization algorithms.

Main Results:

• The enhanced BES algorithm achieved high accuracy (up to 10-18 m) in solving inverse kinematics.
• It demonstrated significant improvements over the original BES algorithm in accuracy, convergence speed, and stability.
• The algorithm showed over 70% improvement in standard deviation and mean for several test functions.

Conclusions:

• The Lévy flight strategy effectively enhances the global search capabilities of the Bald Eagle Search algorithm.
• The proposed algorithm is a robust and efficient tool for addressing complex nonlinear optimization problems, including robotic inverse kinematics.
• The method's practicality was validated through real-world engineering optimization tasks.