In order to handle the complex maintenance tasks of the CFETR’s divertor, a multipurpose overload remote handling robot has been designed by the remote handling team of ASIPP (Institute of Plasma Physics, Chinese Academy of Sciences). The robot system, called CMOR (CFETR multipurpose overload robot), adopts a long cantilever design with nine link combinations. It can cover a ±45°sector and manage a sub-module of the divertor first wall weighing up to 2 tons. Remote handling robots with redundant degrees of freedom and heavy-load characteristics all have problems such as difficulty in calculating collision-free trajectories, reducing the influence of structural deformations on operational safety, and improving control accuracy. For high-efficiency maintenance inside the CFETR, this paper studies an intelligent algorithm for CMOR. Firstly, a motion planning algorithm is optimized. A trajectory reaching any location in the narrow Dshaped CFETR vacuum vessel can be calculated quickly. Secondly, research on real-time structural deformation visualization has been conducted. The deformation data is updated at 2 Hz to detect the state of operation. Thirdly, the structural deformation is predicted and compensated for to improve the control accuracy, and the error is reduced to within ±5 mm. Finally, CMOR realizes efficient, safe, and accurate maintenance of CFETR core components. The main research topics are as follows:
(1) To set objective functions. As the assembly position in the middle maintenance port, the link length of each joint directly affects the workspace and operational flexibility of CMOR. In order to ensure that all the design parameters of CMOR are reasonable, the workspace and operational flexibility are used as objective functions to optimize the installation position and the link length. The final optimized parameters provide a reference for the development of the CMOR prototype.
(2) To form training datasets for neural network (NN). The CMOR has nine degrees of freedom, and its operation environment is a narrow D-shaped CFETR vacuum vessel. It requires a collision-free trajectory during remote handling tasks. Traditional motion planning algorithms have problems such as their low computational success rate and long computational time. In this paper, the shortest paths without collision were used as the training datasets. A supervised neural network was trained by the training datasets to form a motion planning model. The model can calculate a collision-free trajectory quickly with a high computational success rate.
(3) Deformation estimation. After modeling the flexibility of CMOR links and joints and performing a rigid-flexible coupling dynamic analysis, the results show a lot of structural deformation during operation. In this paper, the deformation of the manipulator in different poses was used as a training dataset, while a deformation prediction model using the MLP-Transformer neural network was trained by a training dataset. The deformation prediction model can quickly calculate the structural deformation. The OpenGL library was used to build up the structural simulator to realize the real-time visualization of CMOR structural deformation during maintenance.
(4) To construct the control software. The CMOR deformation prediction model was used to build a structural simulator to monitor the safe state. The deformation prediction model of the endpoints, combined with the fast motion-planning model, formed the CMOR precision control algorithm. The structure simulator and the precision control are integrated into the control software, which is connected with the controller of the manipulator to perform feedback control and realize precise motion planning before operation and real-time status monitoring during operation
