# Algorithm for Determining the Position of a Ship Hull-Cleaning Robot
## 3 Related Works
The robot also uses a sensor system to monitor its condition and its surroundings. Differential pressure sensors measure the pressure inside the suction chamber, allowing it to determine whether the module is attached to the hull surface. In addition, a set of sonar sensors placed around the modules allow for obstacle detection and collision avoidance. Robot control is based on a hierarchical system that consists of three levels. The highest level calculates the route the robot should follow, while the middle level is responsible for reactions to obstacles and maneuvers based on information from the sensors. The lowest level controls motors and devices. In [2], the architecture of the Hornbill robot is described, which was used to autonomously clean the surface of ships by hydroblasting. The robot operates in manual and autonomous modes, which allows the operator to adjust the method of operation depending on the situation. To determine the robot's position on the ship's surface, an ultrasonic-based beacon system is used, where a mobile beacon on the robot calculates its relative 3D position within a network of stationary beacons by sensing ultrasonic waves. The system uses advanced signal processing from various sensors. The input data includes data from the wheel encoders, as well as the orientation provided by the IMU sensor. This data is then processed by a Kalman filter to eliminate noise and obtain a precise robot location. The Hornbill robot is equipped also with a camera used to record video to classify the cleanliness of the surface after cleaning and an IMU responsible for determining the robot's orientation on a vertical surface. Using sensors, the robot generates position data in the form of ROS 3D messages, which contain information about the robot's position in the 3D system. Thanks to the sensor fusion algorithm, it is possible to eliminate noise from the measurement data and obtain more reliable information about the robot's position. The article [6] focuses specifically on the development of a localization system for underwater hull-cleaning robots. The authors present a solution based on optical technology, not using cameras or Lidar in the traditional sense, but rather a customized optical mouse sensor system adapted for underwater use. The primary localization technology discussed is an optical position sensor (OPS), which uses a laser optical mouse sensor combined with a telecentric lens to achieve dead-reckoning navigation on the hull surface. In addition to the OPS, the system integrates data from a landmark recognition sensor (LRS) that uses magnetic flux leakage to detect features such as welds. The OPS provides relative position information between these known features, supporting semi-autonomous or autonomous operation. In summary, the article presents a localization system for a ship hull crawling robot that uses an optical tracking system for continuous relative positioning and a LRS for detecting fixed landmarks. In [3], the development of an autonomous cleaning robot equipped with a hydraulic manipulator arm is presented. The robot utilizes a laser scanner to map the cleaning area and employs path planning algorithms and dynamic simulation to optimize the arm's movements and avoid collisions. The primary localization technology used in the article is an underwater laser scanner mounted on the fifth link of the robot's hydraulic arm. This scanner generates point cloud data on the cleaning area by moving in a lawnmower pattern. The obtained point cloud data are then matched with an existing precise 3D CAD model of the niche area using the Iterative Closest Point (ICP) algorithm to estimate the relative location and positioning between the robot and the target cleaning zone. Furthermore,a multi-beam echo sounder is installed in the robot body to recognize the surrounding topography and provide an approximate location of the niche area.