## 1 Introduction  
A collaborative robot, also known as a cobot, is an industrial robot that can work safely alongside humans in a common workspace. In contrast, autonomous robots are fixed and set up to perform a single task repeatedly. Continuous developments in mobile technology, artificial intelligence, machine vision, and touch technology allow small, lower-powered robots to sense their surroundings and safely perform multiple tasks close to humans. Cobots performing production-related tasks, including assembly, material handling, machine operation, and product quality control, are programmed to protect the safety of their human co-workers. The beginnings of the implementation of industrial robots and manipulators in technological processes are associated with the 1980s [1,2]. Since then, they have undergone a long evolution not only in terms of design but also in terms of control. Cobots operate quickly and reliably, so their application is now commonplace. They are controlled by pre-built rigid codes, but in recent years, the emphasis has been placed on multimodal yet symbiotic methods of communication and control. These methods include voice processing, gesture recognition, haptic interaction, etc. [PERSON] et al. [3] provide an overview of symbiotic assemblies based on human-robot collaboration and indicates future research trends. The manipulator structure is subjected to the action of gravitational forces, caused by the self-weight of the manipulator and the manipulated object, and dynamic forces, generated during the acceleration and deceleration of the motion units. Online monitoring of the magnitudes of these forces helps design actuators appropriately and is also crucial in constructing custom kinematic structures. It is essential to consider not only providing the required strength but also sufficient stiffness due to the high demands on positioning accuracy. These are mainly for manipulators with a large load capacity or with extensive accessibility of the links [4]. In practice, various sensors are used to detect the transmitted load and to accurately adjust the position of the individual parts of the manipulator link. The precise positioning of the robot links is controlled by adjusting the power of the actuators. The driving effect is transmitted between the individual members using connecting parts, enabling the transmission of the force flow. In manufacturing, the actuators are primarily subject to smooth starting and braking requirements, high positioning accuracy, sufficient positional rigidity, minimum weight, minimum size, and suitable spatial arrangement. These requirements aim to ensure a steady, smooth, and frictionless handling and working technological operation of the manipulator. Due to the high inertial forces and speeds, low mass and small dimensions are required [5]. A common feature in robot development is the kinematics design phase. It is widely known that due to manufacturing and assembly tolerances, the actual kinematic parameters of a robot deviate from their nominal values, which are referred to as kinematic errors. [PERSON] et al., in [6], present an online robot self-calibration method based on an inertial measurement unit (IMU) and a position sensor. In this method, the position marker and the IMU need to be firmly attached to the robot tool, which allows the position of the manipulator to be obtained from the position sensor and the orientation from the IMU in real-time. They proposed an efficient approach that uses Kalman filters (KFs) to estimate the manipulator position and orientation. The use of these position (orientation and location) estimation methods will lead to an increase in the reliability and accuracy of the position measurement. Compared to existing self-calibration methods, the most significant advantage of this method is that it does not need any complex steps, such as camera calibration, corner detection, and laser tracking, which makes the proposed robot calibration procedure more autonomous in a dynamic manufacturing environment.