求冗余机器人手臂逆解的反向认知果蝇优化算法

Inverse Solution of Redundant Robot Arm Based on Fruit Fly Optimization Algorithm With Reverse Cognition

为了解决冗余机器人手臂逆解求取中存在的计算量大和求解精度较低等问题,提出一种基于反向认知果蝇优化算法(RCFOA)的解决方法。RCFOA在果蝇算法(FOA)算法的基础上,在进化方程中增加"反向认知"策略,即添加向最差个体学习的改进策略优化进化方程,增强算法跳出局部最优、寻找全局最优的能力。对经典测试函数的仿真结果表明,RCFOA具有更好的全局搜索能力,在收敛速度、收敛可靠性及收敛精度,比其FOA有较大的提高。在机器人手臂逆解求解应用中,能够有效提高精度和稳定性,是求解机器人手臂逆解的一种有效方法。

To solve computationally intensive problems and low solution accuracy of redundant robot arm in inverse solution, a solution method based on fruit fly optimization algorithm with reverse cognition(RCFOA) was proposed. On the basis of FOA, the ‘reverse cognition’ strategy was introduced to RCFOA namely the evolutionary equation is optimized by adding learning the worst individual to it. The ability of the algorithm to break away from the local optimum and to find the global optimum is greatly enhanced. Experimental results of several typical functions show that RCFOA has the advantages of better global search ability, speeder convergence and more precise convergence when compared with FOA. In the application of inverse solution of the robot arm, the accuracy and stability were improved effectively, and is thus applicable to solve the inverse kinematics problem of redundant manipulator.