冗余机械臂逆运动学问题的多策略贪婪蜂群求解

Redundant Manipulator Inverse Kinematic Solution Method Based on Multi-Strategy Greedy Bee Colony Algorithm

为了提高冗余机械臂逆运动学问题求解的位置精度和姿态精度,提出了基于多策略贪婪蜂群算法的求解方法。介绍了KUKA LBR iiwa七自由度冗余机械臂构型,推导了机械臂正运动学的齐次变换矩阵,以减小机械臂末端执行器位置误差和姿态误差为目标建立了优化模型。介绍了标准人工蜂群算法原理并分析了算法存在的缺陷,优化了能够提高蜜蜂多样性的跟随蜂选择策略,以提高跟随蜂局部搜索能力为目的给定了3种位置更新策略,将新算法命名为多策略贪婪蜂群算法。将新蜂群算法应用于逆运动学求解,经仿真验证可知,多策略贪婪蜂群算法的求解质量和求解速度均好于标准蜂群算法,且多策略贪婪蜂群算法在连续多个位姿点逆运动学求解中也具有较高精度,以上结果证明了多策略贪婪蜂群算法在机械臂逆运动学求解中的优越性。

In order to improve the position accuracy and attitude accuracy of the inverse kinematics of redundant manipulator,a solution method based on Multi-Strategy greedy bee colony algorithm is proposed.The configuration of KUKA LBR iwa seven degree of freedom redundant manipulator is introduced,the homogeneous transformation matrix of the forward kinematics of the manipulator is deduced,and the optimization model is established to reduce the position error and attitude error of the end effector of the manipulator.The principle of standard artificial bee colony algorithm is introduced,the defects of the algorithm are analyzed,and the following bee selection strategy which can improve the diversity of bees is optimized.In order to improve the local search ability of following bees,three location update strategies are given,and the new algorithm is named Multi-Strategy greedy bee colony algorithm.The new bee colony algorithm is applied to the inverse kinematics solution.The simulation results show that the solution quality and speed of the Multi-Strategy greedy bee colony algorithm are better than the standard bee colony algorithm,and the Multi-Strategy greedy bee colony algorithm also possesses high accuracy in the inverse kinematics solution of multiple consecutive pose points,The above results prove the superiority of Multi Strategy greedy bee colony algorithm in solving the inverse kinematics of manipulator.