# Autonomous Manipulator of a Mobile Robot Based on a Vision System
## 1 Introduction  
Recent technological advancements allow robotic systems to be more and more autonomous. Much research in the field of autonomous robots is now focusing on autonomous navigation in specific environments or autonomous positioning to perform specific tasks, for example, to grip an object. Improvements in this area are mainly due to the continued innovation in computer vision. The most common solution is to use fiducial markers to estimate the camera pose within an environment in real time. Augmented reality (AR) fiducial markers are widely used in robotics as they are suitable for navigation and positioning purposes. The most common are ARTag [1], ARTag Toolkit [2], and ArUco [3]. The major application of marker-based vision systems is to navigate mobile robots. Knowing the position of the markers on the map, a robot is able to locate itself in space. Another approach assumes that markers can indicate the destination the robot is required to reach. The research presented in [4] involved using ARTag markers to navigate a mobile robot. They helped increase the accuracy of GPS data. Another study concerned with the navigation of mobile robots [5] aimed to develop an algorithm for fusing the wheel odometry and IMU data and a vision system capable of detecting ARTags, estimating the distances to them, and correcting the robot's position determined from the IMU data. The use of marker-based vision systems for robot navigation is also discussed in [6, 7, 8]. Artificial markers can be employed for the navigation and positioning of unmanned aerial vehicles (UAVs). As indicated in [9], the localization of multirotor aerial vehiclesused in indoor applications can be based on visual inertial odometry and a map of markers. In the study of the localization of micro air vehicles (MAVs) [10], the researchers propose fusing the data from sensors with those provided by the vision system using ArUco markers. AR markers were also used in research on the autonomous landing of unmanned aerial vehicles at the desired location, including a mobile platform [11; 12; 13]. Marker-based vision systems are also used to assist in manipulation and positioning. As suggested in [14], a marker tracking system can be integrated into microelectromechanical systems (MEMS) and similar devices to provide real-time data on the position of microscale objects. The research on multirobot industrial systems, e.g., [15], shows that ArUco markers can improve the interaction between a fixed (stationary) robot and a mobile robot, ensuring high-accuracy positioning of the manipulator and high-accuracy selection of details from the mobile platform. As indicated in [16], a manipulator equipped with a marker and camera-based system can accurately position the end-effector to grasp an object and move it to the desired position. The vision system proposed in [17] uses markers to control the position and orientation of excavator arms (booms). The paper [18] presents a system for estimating manipulator positions using ArUco tags. The aim of the study described in this article was to develop an autonomous panel operation system for a mobile robot equipped with a manipulator. In this article, the term robot is used to refer to a platform to which the manipulator is attached. It was assumed that the task performed by the robot would be a selected switch. The position of the manipulator is determined by detecting the positions of the markers located on the panel surface. By determining the locations of the switches on the panel in relation to the markers, the manipulator is able to run appropriate movement sequences to turn them on or off. The two major subsystems responsible for the manipulator's performance are the vision and control systems. The vision system first detects the markers and then calculates the position and orientation of the end-effector with respect to the panel. The control system uses the data to move the end-effector to a desired position in the coordinate system so that the task can be performed with the highest accuracy possible.