Inverse kinematics solution for robotic manipulator based on extreme learning machine and sequential mutation genetic algorithm

Abstract

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.

Two Optimized General Methods for Inverse Kinematics of 6R Robots Based on Machine Learning

“…As shown in Figure 2, k _ optimal is mainly distributed within [20,120]. Statistical data show that, for 95.4% of the poses, the minimum number of iterations corresponding to k _ optimal can be controlled under 20.…”

“…Various soft computing methods [19][20][21][22][23][24][25][26] based on the artificial neural network and genetic algorithm are also the research hotspots in recent years. A comparative study between different soft computing-based methods (artificial neural network, adaptive neuro-fuzzy inference system, and genetic algorithms) applied to the problem of inverse kinematics is presented in [19].…”

“…A comparative study between different soft computing-based methods (artificial neural network, adaptive neuro-fuzzy inference system, and genetic algorithms) applied to the problem of inverse kinematics is presented in [19]. A genetic algorithm based on extreme learning machine and sequential mutation is presented in [20]. A hybrid Taguchi deoxyribonucleic acid (DNA) swarm intelligence for solving the inverse kinematic redundancy problem is presented in [21].…”

“…, and C matrices are directly obtained by the derived expressions M matrix is obtained by equation(14)and calculate eigenvalues and eigenvectors θ 1~θ6 are calculated by equations(17)-(20) outputEnd Flowchart of the optimized algorithm.…”

Model approach of a robotic manipulator 3DOF using Machine Learning

“…Some works the ELM network was used to solve problems of reverse kinetics in other types of robots like: [10] The ELM network has several advantages over the others, such as: the input weights and the polari-zation of the hidden layer are chosen randomly, another advantage is that the weights of the output layer are determined analytically [9]. In the following algorithm a step-by-step of the network will be shown.…”