## 1 Introduction
Next, the 3D model will be imported into Simscape to generate a multibody system representation. The multibody system will include the main body of the mobile platform and the interchangeable wheels, both conventional and Mecanum. The mass properties and inertial properties of each component will be assigned based on the physical characteristics of the mobile platform and wheels. The tire friction model proposed by [25; 26; 27; 28; 29; 30] will be adapted and incorporated into the dynamic model to accurately represent the resistive forces experienced by the platform during motion. This model will consider both static and dynamic friction, and friction coefficients. The kinematics for mobile robots with four-wheel drive will be used to develop the dynamic equations of motion for the mobile platform [22; 23; 24; 25]. Furthermore, the model will account for the slip of the platform's tires from [26; 27; 28; 29; 30], which is crucial for accurately predicting the behavior of the mobile platform. To validate the proposed dynamic model, experimental data will be collected by conducting physical experiments on a prototype of the mobile platform. The experiments will involve measuring the current consumed by the platform with different wheel configurations and under various operating conditions. Additionally, simulation studies will be conducted in Simscape Simulink using the developed dynamic model. The simulations will involve analyzing the behavior of the mobile platform with different wheel configurations, such as using conventional wheels for straight-line motion and when turning and Mecanum wheels for omni-directional motion. The results from the simulations will be compared with the experimental data to assess the accuracy of the dynamic model. The development of accurate dynamic models for autonomous mobile platforms is essential for various applications, including robotics, automation, and transportation systems. These dynamic models allow for precise analysis and prediction of the resistive forces experienced by the mobile platform during motion [26; 27; 28; 29; 30]. In [28], [PERSON] explores the use of skin-like hydrogel sensors for perception in cable-driven continuum robots. It investigates how these sensors can provide feedback for tasks such as object detection, manipulation, or environment exploration in robotic systems. In [29], the authors involve the design and control of a robotic system inspired by the biomechanics of a mantis shrimp. It includes aspects such as mechanical design, actuation mechanisms, and control algorithms tailored to mimic the locomotion and manipulation capabilities of the mantis shrimp. The paper [30] aims to develop a theoretical framework for understanding fuzzy information granulation, which plays a central role in human reasoning and fuzzy logic. It explores how fuzzy sets and fuzzy logic can be used to model human cognitive processes and decision-making. In [31], [PERSON] discusses a controller designed for an omnidirectional AGV equipped with Mecanum wheels. This study emphasizes the ability to navigate efficiently in congested areas by detecting slips and avoiding obstacles through the integration of fuzzy logic with sensor inputs for dynamic environment adaptation. Detection behavior for Mecanum-wheeled AGVs emphasizes a specialized fuzzy logic controller for slip detection and navigation efficiency in crowded environments, optimizing the AGV's performance through intelligent sensor data interpretation and control logic. In [32], [PERSON] focuses on modeling the dynamic behavior of a beaver-like robot's tail and analyzing its motion. This study investigates how the tail contributes to the robot's overall locomotion and maneuverability, drawing inspiration from the biomechanics of beavers for robotic design and control. In [33], [PERSON] presents a fuzzy PID control strategy for improving the maneuverability of a Mecanum-wheeled robot.