## 1 Introduction
Providing these estimations to the fast-rate motion controller, a proper control signal may be generated so as to achieve a satisfactory path-following behavior. The proposed NUDREKF introduces a flexible formulation based on the use of a diagonal matrix, which is updated at fast rate from the current, existing measurements, allowing any possible sampling pattern. This feature results in a straightforward algorithmic implementation. Few works on (uniform) DREKF can be found in literature related to autonomous vehicle frameworks. For instance, the DREKF is used to better estimate unavailable system states in unmanned aerial vehicles [20] to manage uncertainty in an obstacle tracking application [21], to fuse output variables sensed at different rates in a self-driving vehicle [22] and in a two-wheeled mobile robot [23]. Nevertheless, to the best of the authors' knowledge, no work is available coping with the problem of nonuniform measurements via the so-called NUDREKF. Only in [24; 25] is this kind of filter used to deal with nonuniform and delayed measurements, but applied to numerical examples and case studies out of the autonomous vehicle field. In order to authentically reproduce the expected real behavior of the robotic platform, Simscape Multibody is used in this work. This multibody modeling tool is an extension of Matlab/Simulink, where complex physical bodies and their interactions can be intuitively defined so as to realistically simulate system dynamics. In this way, the robot model can include nonlinearities present in the real vehicle such as dead-zone, saturation, encoder resolution, and wheel sliding. These model parameters can be previously validated by comparing real and simulated behavior. Simscape Multibody has been employed in some recent studies related to autonomous vehicle frameworks. For instance, in [26], an anticlock braking system (ABS) control for a vehicle is simulated; in [27], a mathematical model needed for torque determination during robot locomotion is tested and validated; in [28], simulation results for even and uneven surfaces are compared for a tread robot; in [29], a mechatronic interface for mobile manipulators is developed; in [23], a path-following control for a two-wheeled mobile robot is simulated. To the best of authors' knowledge, few works on the simulation of mecanum-wheeled mobile platforms via Simscape Multibody can be found in literature. Some examples may be [30; 31], where different motion models including multiple contact forces for this kind of robot are simulated. Summarizing, the main contributions of the work are as follows:  
* Consideration of a NUDREKF, which enables us to generate fast-rate state estimates from nonuniform, slow-rate measurements to be supplied to the fast-rate motion controller in order to reach a satisfactory path-following behavior. * Modification of the Pure Pursuit path-stacking algorithm to improve adaptability to the change in turning and curvature sections of paths. * Development of a powerful simulation tool, which takes into account complex modeling aspects to realistically represent the mobile platform movement. The paper is organized as follows. Section 2 describes the problem scenario and the proposed control solution. Section 3 is devoted to mathematical aspects such as kinematic modeling of the process (Section 3.1), formulation of the NUDREKF (Section 3.2), and presentation of the modified version of the Pure Pursuit algorithm (Section 3.3). Section 4 presents the simulation tool developed, focusing on the description of the nonlinear aspects of the model. Section 5 introduces the cases simulated and the results obtained, which are analyzed in detail via some cost indexes. Finally, some conclusions summarize the present work in Section 6.