## 9 Conclusions  
This study presents significant practical implications for the design of mobile platforms, emphasizing the importance of selecting suitable driving types and control strategies designed to specific operational requirements such as energy efficiency, terrain traversal, and maneuverability. By developing and validating a dynamic model for mobile platforms, particularly those equipped with Mecanum wheels, this paper offers valuable insights into optimizing their design and control systems. This research contributes to the robotics field by introducing a comprehensive approach to understanding and enhancing the dynamics and control of autonomous mobile robots. Unlike traditional methods relying on differential equations, this study employs Simulink's block-based diagrams for a simplified development process. Notably, the dynamic model incorporates friction forces on the wheels, crucial for accurate energy monitoring. Through experimental validation, this study ensures the accuracy and reliability of the dynamic model. Moreover, it explores the integration of 3D models from SolidWorks into Simulink, enabling detailed representations of mobile platforms. By formulating mathematical models for both conventional and Mecanum wheel configurations, the paper facilitates the development of energy-efficient driving strategies. The decomposition of resistive forces using the Jacobian matrix enables precise simulation of electrical current consumption during robot motion. This paper's focus on energy consumption monitoring and trajectory tracking, coupled with fuzzy decision algorithms, proposes a method for determining optimal locomotion modes for Mecanum wheel-equipped mobile platforms. As elements of novelty, this paper presents the development of a dynamic model for mobile platforms based on importing a 3D assembly into Simscape-Multibody. The dynamic model considers the friction forces acting on the wheels. Different scenarios in which friction forces occur and act on the mobile platform have been analyzed and described in detail: front-back differential motion and rotation, front-back omnidirectional motion, rotation, and lateral motion. This analysis and description of wheel friction forces are novel elements because few works detail these five cases of locomotion. The detailed description of friction forces is crucial because friction is the component of resistance forces that distinguishes the energy consumption between different locomotion types the most. If this friction force model were not correctly modeled, the energy consumption for the five types of movement would not show significant differences. The dynamic model and the friction forces model were experimentally validated, and this aspect was presented in Section 8. The dynamic model of the mobile platform is necessary for a user to identify the energy consumption for specific locomotion types. The cost function is important in monitoring energy consumption. A fuzzy logic-based model necessary and useful in selecting a locomotion mode for a particular trajectory has been developed. Based on input parameters such as desired energy consumption, desired displacement accuracy, necessary mobility, and travel time, a trajectory can be divided into several sectors, and the robot can use different locomotion modes for each sector. This research does not focus on controlling and precisely trackingthe imposed trajectory because numerous works present these aspects. The novelty lies in choosing the driving mode of the imposed trajectory. As future work, improving the fuzzy model and testing the entire process on a trajectory of high complexity is proposed. In conclusion, this research significantly advances the field of autonomous mobile robotics by providing a comprehensive framework for dynamic modeling and energy-efficient operation. The experimental validation of dynamic models and thorough analysis of resistive forces promise substantial progress in the practical implementation of autonomous mobile platforms. Conceptualization, M.C., R.-E.B., S.-G.R.