Abstract
In this paper, the shortest path problem of manipulator path planning is transformed into a linear programming problem, and solved by zeroing neural network (ZNN). Firstly, the method of constructing the zeroing neural dynamics is given, and the ZNN model is constructed for shortest path problem of manipulator. Then, the Lyapunov method is utilized to prove the stability of the ZNN model. Finally, the ZNN model is applied to the path planning of manipulator to generate an optimal planning path. The simulation results show that the proposed method can effectively realize the optimal path planning of the manipulator.
Highlights
In recent years, more and more scholars pay attention to the path planning of manipulator
Adamu carried out global motion planning algorithm by considering ways to improve the speed, using particle swarm optimization (PSO) technology to converge to the global minimum, and using custom algorithm to generate search space coordinates [7]
For the highly nonlinear two-degree-of-freedom manipulator system, the path planning of the manipulator is expressed as a linear equation and the corresponding linear equation is solved by the method of the tension neural network
Summary
More and more scholars pay attention to the path planning of manipulator. In this paper, a ZNN method for solving the shortest path problem of a robot arm is proposed. This method expresses the optimal path planning problem as a linear programming problem, and solves the linear programming problem by. ZNN can solve the problem of estimation error convergence effectively and improve the computational efficiency and accuracy of model [9]. Before ending this introductory section, the main contributions of this paper can be described as follows: there are 3 important contributions in this paper. The shortest path problem is solved when applied to the two-degree-of-freedom manipulator
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