Zhao Guojun, Tao Bo, Jiang Du, Yun Juntong, Fan Hanwen
Key Laboratory of Metallurgical Equipment and Control Technology of Ministry of Education, Wuhan University of Science and Technology, Wuhan 430081, China.
Precision Manufacturing Research Institute, Wuhan University of Science and Technology, Wuhan 430081, China.
Biomimetics (Basel). 2024 Oct 15;9(10):627. doi: 10.3390/biomimetics9100627.
The inverse kinematics of robotic manipulators involves determining an appropriate joint configuration to achieve a specified end-effector position. This problem is challenging because the inverse kinematics of manipulators are highly nonlinear and complexly coupled. To address this challenge, the bald eagle search optimization algorithm is introduced. This algorithm combines the advantages of evolutionary and swarm techniques, making it more effective at solving nonlinear problems and improving search efficiency. Due to the tendency of the algorithm to fall into local optima, the Lévy flight strategy is introduced to enhance its performance. This strategy adopts a heavy-tailed distribution to generate long-distance jumps, thereby preventing the algorithm from becoming trapped in local optima and enhancing its global search efficiency. The experiments first evaluated the accuracy and robustness of the proposed algorithm based on the inverse kinematics problem of manipulators, achieving a solution accuracy of up to 10-18 m. Subsequently, the proposed algorithm was compared with other algorithms using the CEC2017 test functions. The results showed that the improved algorithm significantly outperformed the original in accuracy, convergence speed, and stability. Specifically, it achieved over 70% improvement in both standard deviation and mean for several test functions, demonstrating the effectiveness of the Lévy flight strategy in enhancing global search capabilities. Furthermore, the practicality of the proposed algorithm was verified through two real engineering optimization problems.
机器人操纵器的逆运动学涉及确定合适的关节配置以实现指定的末端执行器位置。这个问题具有挑战性，因为操纵器的逆运动学高度非线性且耦合复杂。为应对这一挑战，引入了秃鹰搜索优化算法。该算法结合了进化技术和群体技术的优点，使其在解决非线性问题和提高搜索效率方面更有效。由于该算法有陷入局部最优的倾向，引入了莱维飞行策略来提高其性能。该策略采用重尾分布来生成远距离跳跃，从而防止算法陷入局部最优并提高其全局搜索效率。实验首先基于操纵器的逆运动学问题评估了所提算法的准确性和鲁棒性，实现了高达10 - 18米的求解精度。随后，使用CEC2017测试函数将所提算法与其他算法进行了比较。结果表明，改进后的算法在准确性、收敛速度和稳定性方面明显优于原始算法。具体而言，对于几个测试函数，其标准差和均值均提高了70%以上，证明了莱维飞行策略在增强全局搜索能力方面的有效性。此外，通过两个实际工程优化问题验证了所提算法的实用性。
