## 7 Conclusions  
As we can summarize from the model and error analysis of the system, the system accuracy of the multiple linear regression method is lower, since the basis of using the multiple linear regression model to process experimental data is that the system is linear. However, some uncertain nonlinear factors in the actual experimental process result in inaccurate models and larger errors. Nevertheless, some advantages of the multiple linear regression method must be acknowledged; a smaller scale of calculation and higher real-time performance must be mentioned, and low-computing-power equipment can be used to build platforms such as single-chip microcomputers. Moreover, the system can be directly assumed as a black box without the tedious theoretical derivation. At the same time, with a higher system accuracy, the drawbacks of the artificial neural network method are also obvious. Larger scales of calculation and an equipment platform with higher computing power are required. This shortcoming is particularly prominent in the underwater robot with narrow internal space.  
In this paper, to estimate the disturbance of force and torque from the wave and current, an artificial lateral-line system is constructed by using multi-sensor pressure nodes, which are inspired by the lateral-line system in fish. The model of underwater vehicle equipped with an artificial lateral-line system is theoretically analyzed from the perspective of mechanics, and an experimental platform for the artificial lateral system is built. Different experiments are conducted for the sway, surge, and yaw motion components. Experimental data are preprocessed by the Butterworth filter, and models based on the linear regression method and the artificial neural network can be obtained respectively on the basis of physical models and experimental data. The accuracy, advantages and disadvantages of these two models are briefly explained at the end.  
The work in this paper provides a theoretical basis and implementation method for underwater robots to obtain a perception system of the water environment, which can be used in antidisturbance control of underwater robots. The stability of underwater robots will be improved through that method, which will make a difference for underwater tasks such as underwater target recognition and underwater manipulator operation.  
In following work, the artificial lateral-line system will be further improved. Combined with the artificial lateral-line system and disturbance observer in the control algorithm method, the external and internal disturbances of the underwater robot will be observed at the same time, which will strengthen the antidisturbance ability of the underwater robot.  
Conceptualization, W.C. and S.K.; methodology, S.K.; software, J.Y.; validation, S.K., W.C. and J.Y.; formal analysis, S.K.; investigation, S.K.; resources, S.K.; data curation, J.Y.; writing--original draft preparation, S.K.; writing--review and editing, W.C.; visualization, S.K.; supervision, W.C.; project administration, W.C.; funding acquisition, W.C. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China under grant number [61633002].  
Not applicable.  
This work was supported by the National Natural Science Foundation of China under grant number [61633002].  
The authors declare no conflict of interest.