Research on Complex Motion Path Optimisation Based on PDE Constraints and DeepONet Neural Network Models

Aiming at the key motion path and speed control problem in a traditional folk performance, this paper designs a comprehensive optimization scheme. Combined with the constraints of partial differential equation (PDE), the position and velocity changes of each time node are calculated to ensure the smoothness of the motion and the rationality of the speed. In order to further improve the optimization effect, alternate direction multiplier method (ADMM) and DeepONet neural network model are introduced to realize the fine adjustment of path planning and speed control. The results show that this scheme not only improves the performance's appreciation, but also significantly enhances the execution's accuracy and operability.