## 2 State of the Art Review
### Motion Control of Wheeled Mobile Robots  
The motion control system of wheeled mobile robots often has a hierarchical structure, which may include a wheels' controller and a mobile platform controller. The mobile platform controller may be based on the measurement of linear and angular velocities or on the measurement of position and course, i.e., pose. The pose controller is also called the kinematic controller, e.g., in [PERSON] and [PERSON], 2023 [14], because it uses only the kinematic parameters of the robot's movement. It is more advantageous to use the robot's pose measurement and rely on the pose controller, because, in the case of a velocity controller, errors related to the estimation of the robot's pose based on the velocity measurement accumulate over time. Therefore, the robot may deviate from the desired path, which may not be noticed by the velocity controller of the mobile platform. The wheels' controller may be based on PID control or have a more complex structure, for instance taking into account the dynamics model of the robot. In such a case, this type of controller can be called a dynamic controller--see [PERSON] and [PERSON], 2023 [14]. Using the dynamics model and methods such as robust or adaptive control, one can achieve a high accuracy of the tracking control in the absence of wheel slip. For example, in the paper by [PERSON] et al., 2023 [15], robust control was proposed for the path tracking control of autonomous vehicles. The movement of mobile robots is often related to the occurrence of variable working conditions, which may include variable ground properties, changes in the mass and position of the mass center of the robots due to the transport of goods, etc. For example, in the article by [PERSON], 2013 [PERSON] (2013), the influence of the change of the type of ground on the robot's movement is analyzed. From the point of view of motion control, such changes can be interpreted as parametric disturbances. This is another argument for using control systems based on the robot dynamics model, such as adaptive control. This method enables the estimation of the current parameters of the robot model, reducing the model uncertainty. Artificial intelligence techniques can also be applied to approximate the nonlinear dependencies occurring in the dynamics model, including those based on fuzzy logic systems, artificial neural networks. and genetic algorithms, an example of which is the paper by [PERSON] and [PERSON], 2023 [PERSON] and [PERSON] (2023). In the case of movement in slipping conditions, relying only on the wheels' control is insufficient, as shown in the article by [PERSON], 2013 [PERSON] (2013). In addition, control systems using artificial intelligence techniques have an advantage over those using an analytical approach in that they can additionally provide resistance to non-parametric disturbances resulting from the inaccurate knowledge of the structure of the robot model. For example, in the case of artificial neural networks, a robot dynamics model can be obtained as a result of the learning process. In the paper by [PERSON] and [PERSON], 2023 [PERSON] and [PERSON] (2023), in addition to the kinematic pose controller, the adaptive fuzzy control method is applied to control the movement of a four-wheeled skid-steered robot. This approach allowed for achieving a high accuracy of tracking control despite the occurring slip of wheels. In this case, the control system allows for the compensation of nonlinearities resulting from the dynamics of the robot. In the paper by [PERSON] et al., 2021 [PERSON] et al. (2021), a kinematic robot pose controller, a controller based on robot dynamics, and a nonlinear disturbance observer are used. The proposed approach, as a result of the inclusion of the wheels' skidding and slipping in the control system, allowed us to achieve a high accuracy of the robot's movement, which was confirmed at the experimental research stage. In the work by [PERSON] et al., 2023 [PERSON] et al.