【摘要】 针对一种冗余自由度的双臂协作机器人手臂的逆运动学求解问题，在果蝇优化算法(Fruit Fly Optimization Algorithm, FOA)的基础上结合Harris鹰捕食的行为，提出了一种改进的果蝇算法(HHFOA)。受到Harris鹰的捕食、突袭策略的启发，HHFOA算法的果蝇嗅觉搜索机制中添加了探索和开发阶段，在视觉更新机制中同时保留了全局最优解和个体历史最优解。首先通过运动学方程建立机械臂末端的位姿误差函数，并将其最小值作为优化目标，然后分别使用FOA、LGMS-FOA、IFOA和HHFOA对该函数进行求解。对比四种算法的收敛结果，证明了HHFOA的收敛精度、稳定性和成功率更高，在解决机械臂逆运动学问题上更具优势。更多还原

【Abstract】 Aiming at the problem of solving the inverse kinematics of a dual-arm collaborative robot arm with redundant degrees of freedom, based on the fruit fly optimization algorithm(FOA) combined with the predation behavior of the Harris’ s hawks, an improved fruit fly optimization algorithm(HHFOA) is proposed. Inspired by the predation and raid strategy of Harris’ s Hawks, the HHFOA adds an exploration and development stage to the fruit fly olfactory search mechanism, and retains both the global optimal solution and the individual historical optimal solution in the visual update mechanism. Firstly, the pose error function of the end of the manipulator is established by kinematics equations, and its minimum value is used as the optimization target, and then FOA, LGMS-FOA, IFOA and HHFOA are used to solve the function. Comparing the convergence results of the four algorithms, it is proved that HHFOA has higher convergence accuracy, stability and success rate, and has more advantages in solving the inverse kinematics problem of the robotic arm.更多还原