This study designed an algorithm for the intelligent control of the motion of a mobile robot with mecanum wheels. After reviewing the model kinematics and dynamics of the robot, we conducted a synthesis of the neural control algorithm to determine network weight adaptation, according to Lyapunov stability theory. Using a MATLAB/Simulink computing environment, we developed a numerical simulation for the implementation of the robot’s motion path with parametric disturbances acting on the control object. To determine the quality of the implementation of the desired motion path, a numerical test of the robot’s motion, controlled with the use of a PD controller, was conducted. The proposed control algorithm was verified on a laboratory stand equipped with a dSpace DS1103 controller board and a Husarion Panther four-wheeled mobile robot with mecanum wheels. The conducted research confirmed the improved implementation of the desired motion path by a robot controlled with the use of an intelligent control system.

The symmetry of the omnidirectional robot motion abilities around its central vertical axis is an important advantage regarding its driveability for the flexible interoperation with fixed conveyor systems. The paper illustrates a Hardware in the Loop architectural approach for integrated development of an Ominidirectional Mobile Robot that is designed to serve in a dynamic logistic environment. Such logistic environments require complex algorithms for autonomous navigation between different warehouse locations, that can be efficiently developed using Robot Operating System nodes. Implementing path planning nodes benefits from using Matlab-Simulink, which provides a large selection of algorithms that are easily integrated and customized. The proposed solution is deployed for validation on a NVIDIA Jetson Nano, the embedded computer hosted locally on the robot, that runs the autonomous navigation software. The proposed solution permits the live connection to the omnidirectional prototype platform, allowing to deploy algorithms and acquire data for debugging the location, path planning and the mapping information during real time autonomous navigation experiments, very useful in validating different strategies.