## 1 Introduction  
The necessity for increasing the efficiency of deployment of autonomous or hybrid unmanned underwater vehicles (UUV) with multilink manipulators (MM) for surveying large areas, detecting marine objects and performing manipulative operations with them has spurred the development of a multilevel system that would provide control over complex dynamic objects. The operation of robotic vehicles with such a system does not require a special support vessel or numerous maintenance personnel; additionally, their movements are not limited to the length of the umbilical cable, and they can travel dozens of kilometers in search of target objects [1; 2; 3]. A MM mounted on an UUV significantly extends the range of its functional capabilities to also include sediment sampling from specified areas, collection of marine organisms, installation of beacons and various equipment and their cleaning and maintenance [3; 4; 5; 6]. Researchers have designed a number of methods and algorithms for automating various manipulative operations of UUV. As shown in Figure 1, these methods can be divided into a few major groups. The methods explained in the studies [7, 8] provide an evaluation of the roughness of the bottom topography in the working area and make a decision as to whether the bottom is suitable for safe manipulation by the time the UUV approaches the object. Their efficiency has been confirmed experimentally. Prior to the operations, the object is identified on the basis of data received from the onboard sonars [9, 10], and then the UUV is sent to the object. Simultaneously, a site for stabilized hovering of the UUV is selected in such a way that the working zone of the manipulator allows correct execution of the required operations and avoidance of collisions of the vehicle with the bottom or object [8]. Efficient methods have been developed to keep the UUV in the hovering (or dynamic positioning) mode near the object [11, 12, 13, 14] using the vehicle's thrusters, these methods provide compensation for the negative dynamic effects from unknown underwater currents and from the working manipulator. In this case, the force and torque effects from the MM are calculated in real time mode by the algorithm for solving the inverse dynamics problem (IDP) for underwater MM [13]. The parameters of manipulator links' interaction with the aquatic environment included in this algorithm are determined experimentally by the method [15]. The shape and location of the target object relative to the UUV are determined with high accuracy using the data received from the multibeam echosounder sonars and stereo cameras, which constitute an onboard machine vision system (MVS) [16, 17, 18]. Various MVS that produce output data in the form of point clouds have long and widely been used in both industrial and mobile robotics [19]. The major advantages of such systems are the high-speed response, high accuracy and high scanning density, which allow reconstructing a 3D model of the workspace on the vehicles' onboard computer. However, the underwater environment somewhat restricts the use of laser scanners or stereo cameras as MVS, due to optical distortions and possible high turbidity of the water. Nevertheless, with special algorithms for filtering and processing distorted information applied, optical MVS can also be used underwater [20]. Before processing the obtained point clouds, the photo and video images pass through an image processing system [21, 22]. The latter makes it possible to recognize the object with approximately known dimensions and color in an obtained  
Figure 1: Block diagram of a multilevel control system of UUV for automated manipulative operations. image using special color filters. This significantly accelerates the process of search and identification of a previously known object by devices with lower computing capacities.