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.

翻译：在三维空间中，自由度超过六的机器人属于冗余机器人。针对灵巧工作空间内的给定目标点，冗余机器人允许存在多种构型方案。多解特性有助于解决工作空间约束问题，如轨迹规划中的避障与能量最小化。这类冗余机器人的逆运动学求解具有高度复杂性。本研究通过对存在位置误差的不同元启发式优化算法进行对比，旨在识别出一种能实现机器人末端执行器高精度定位的优化算法。本文利用Denavit-Hartenberg参数的正向运动学模型，将最新提出的元启发式优化算法应用于超冗余九自由度机械臂的逆运动学求解，并比较了各算法性能。对比研究表明，秃鹰搜索算法相较于其他元启发式算法具有更优表现：在九自由度机械臂的目标位置求解中，该算法能以极高精度实现定位且位置误差最小。