冗余度机械手逆运动学果蝇优化算法的改进

逆运动学问题是冗余度机器人运动控制、轨迹规划和动力学分析的基础,也是机器人学中最重要的问题之一。以末端执行器位姿的误差最小为优化目标,建立适应度函数,将求解冗余度机械手的逆运动学问题转化为一个等价的优化问题,在优化算法的基础上通过杂交变异果蝇优化算法(HMFOA)进行冗余度机械手逆运动学问题的求解。采用嗅觉搜索杂交突变机制和视觉搜索的动态实时更新机制,有效解决果蝇优化算法(FOA)的收敛问题,并提高算法的收敛速度。为进一步验证HMFOA的有效性,在七自由度机械手上对HMFOA进行了测试,将其结果与FOA、LGMS-FOA和AE-LGMS-FOA等算法进行了比较,证明HMFOA能有效地解决冗余机械手的逆运动学问题。

Improvement of Fruit Fly Optimization Algorithm for Inverse Kinematics of Redundant Manipulator

Inverse kinematics is the basis of motion control, trajectory planning and dynamics analysis of redundant robots, and it is also one of the most important problems in robotics. Taking the minimum error of the position and pose of the end effector as the optimization objective, the fitness function was established, and the inverse kinematics problem of redundant manipulator was transformed into an equivalent optimization problem. Based on the swarm intelligence optimization algorithm, the hybrid mutation Drosophila optimization algorithm (HMFOA) was applied to solve the inverse kinematics problem of redundant manipulator. Using olfactory search hybrid mutation mechanism and visual search dynamic real-time update mechanism could effectively solve the convergence problem of fruit fly optimization algorithm (FOA) and improve the convergence speed of the algorithm. In order to further verify the effectiveness of HMFOA, HMFOA was tested on a 7-DOF manipulator, and the results were compared with FOA, LGMS-FOA and AE-LGMS-FOA. The experimental results show that HMFOA can effectively solve the inverse kinematics problem of redundant manipulators.