## 1 Introduction
These methods are not computationally expensive. However, like numerical methods, they do not take into account the possibility of the multilink manipulator entering special positions. In [7; 14], the Artificial Neural Networks (ANN) approach to the control of multilink manipulators was proposed. The methods based on the application of the ANN approaches allow unambiguously solving the IKP with a given accuracy and taking into account the constraints on the generalized coordinates of the multilink manipulator having excessive degrees of mobility. When solving manipulation tasks, including underwater ones, it is often impossible to determine in advance the exact shape of the object of interest, as well as the mutual location of the underwater robot and this object. Then, it is necessary to form the manipulator's control bypassing the stage of testing. It can lead to self-collisions. When implementing copying control of manipulators [14], it is very likely that self-collision of manipulators will occur (Figure 3). A number of methods additionally use sensors installed on manipulators [15; 16]. Sensors are usually implanted directly inside or outside robot arms. An example of such a method is, for example, the \"skeleton algorithm\" [17]. This algorithm requires sensory information about the position of each link during movement in order to plan evasive trajectories before possible collisions. For anthropomorphic manipulators, there is a problem of collision of all its nodes. For example, a collision may occur between the first link of the left manipulator and the penultimate link of the right manipulator. It is not advisable to install sensors on all links and the sensor must monitor the space around it 360 degrees. Sensor methods are expensive and cannot be used in simulation studies [18]. The most modern collision detection methods are based on analytical approaches [19; 20; 21; 22; 23; 24; 25; 26]. In [7], an ANN solution on the basis of the \"Contextual\" ANN to predicting the self-collision  
Figure 3: The control scheme with the copying suit. of multilink manipulators is proposed. This method implements a regression approach. The proposed solution results in high accuracy and allows the possibility of self-collision in an online mode to be checked. From the point of view of the authors, the study of the possibility of the ANN solution based on the classification problem to predict the self-collision of multilink manipulators is interesting. This approach will allow us to give an unambiguous answer to the following question: will there be a self-collision or not? Based on the fact that due to the difficulties in analytically finding the global extremum, the construction of the ANN is often experimental in nature; in this paper, we will present a description of the construction of the ANN by experimentally selecting its parameters to solve the classification problem as a method for predicting self-collision. The main advantages of this method are the high speed of calculations and the ability to use this method without calculating the parameters and dimensions of the robot. This makes it possible to use an ANN approach for manipulators to which there is no physical access and, accordingly, it is impossible to calculate the inverse kinematics problem. The ANN proposed in this paper characterizes the position of manipulators in space and classifies the state of their location. The rest of this paper is organized as follows. Description of the class of systems and the problem statement are presented in Section 2. Section 3 introduces the ANN training parameters. At the same time, we provide the development of the ANN in Section 4. Finally, we summarize the contributions of our work and discussion in Section 5.