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首页> 外文期刊>International Journal of Advanced Robotic Systems >Inverse kinematics solution for robotic manipulator based on extreme learning machine and sequential mutation genetic algorithm
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Inverse kinematics solution for robotic manipulator based on extreme learning machine and sequential mutation genetic algorithm

机译：基于极限学习机和序列突变遗传算法的机械臂逆运动学解决方案

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This article presents an intelligent algorithm based on extreme learning machine and sequential mutation genetic algorithm to determine the inverse kinematics solutions of a robotic manipulator with six degrees of freedom. This algorithm is developed to minimize the computational time without compromising the accuracy of the end effector. In the proposed algorithm, the preliminary inverse kinematics solution is first computed by extreme learning machine and the solution is then optimized by an improved genetic algorithm based on sequential mutation. Extreme learning machine randomly initializes the weights of the input layer and biases of the hidden layer, which greatly improves the training speed. Unlike classical genetic algorithms, sequential mutation genetic algorithm changes the order of the genetic codes from high to low, which reduces the randomness of mutation operation and improves the local search capability. Consequently, the convergence speed at the end of evolution is improved. The performance of the extreme learning machine and sequential mutation genetic algorithm is also compared with that of a hybrid intelligent algorithm, and the results showed that there is significant reduction in the training time and computational time while the solution accuracy is retained. Based on the experimental results, the proposed extreme learning machine and sequential mutation genetic algorithm can greatly improve the time efficiency while ensuring high accuracy of the end effector.
机译：本文提出了一种基于极限学习机和顺序突变遗传算法的智能算法，用于确定具有六个自由度的机械手的逆运动学解。开发该算法可最大程度地减少计算时间，同时又不会影响末端执行器的精度。在提出的算法中，首先通过极限学习机计算初步的逆运动学解，然后通过基于序列突变的改进遗传算法对解进行优化。极限学习机随机初始化输入层的权重和隐藏层的偏差，极大地提高了训练速度。与经典遗传算法不同，顺序突变遗传算法将遗传密码的顺序从高变低，从而降低了变异操作的随机性，提高了局部搜索能力。因此，改进了进化结束时的收敛速度。还将极限学习机和顺序突变遗传算法的性能与混合智能算法的性能进行了比较，结果表明，在保持求解精度的同时，训练时间和计算时间显着减少。基于实验结果，提出的极限学习机和序列突变遗传算法可以在保证末端执行器高精度的同时，极大地提高时间效率。

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