In the field of artificial intelligence, control systems for mobile robots have undergone significant advancements, particularly within the realm of autonomous learning. However, previous studies have primarily focused on predefined paths, neglecting real-time obstacle avoidance and trajectory reconfiguration. This research introduces a novel algorithm that integrates reinforcement learning with the Deep Q-Network (DQN) to empower an agent with the ability to execute actions, gather information from a simulated environment in Gazebo, and maximize rewards. Through a series of carefully designed experiments, the algorithm’s parameters were meticulously configured, and its performance was rigorously validated. Unlike conventional navigation systems, our approach embraces the exploration of the environment, facilitating effective trajectory planning based on acquired knowledge. By leveraging randomized training conditions within a simulated environment, the DQN network exhibits superior capabilities in computing complex functions compared to traditional methods. This breakthrough underscores the potential of our algorithm to significantly enhance the autonomous learning capacities of mobile robots.

During the Industry 4.0 era, the open source-based robotic arms control applications have been developed, in which the control algorithms apply for movement precision in the trajectory tracking paths based on direct or reverse kinematics. Therefore, small errors in the joint positions can summarize in large position errors of the end-effector in the industrial activities. Besides the change of the end-effector position for a given variation of the set-point in manipulator joint positions depends on the manipulator configuration. This research proposes a control based on Proportional Derivative (PD) Control with gravity compensation to show the robustness of this control scheme in the robotic arm’s industrial applications. The control algorithm is developed using a low-cost board like Raspberry Pi (RPI) where the Robot Operating System (ROS) is installed. The novelty of this approach is the development of new functions in ROS to make the PD control with gravity compensation in low-cost systems. This platform brings a fast exchange of information between the Kuka™ youBot robotic arm and a graphical user’s interface that allows a transparent interaction between them.

Current industry must improve the day-to-day control and industrial communications of its processes in order to bring itself closer to the Industry 4.0 paradigm. To attain these improvements, which aim towards obtaining agile and intelligent manufacturing systems, the IEC-61499 standard is considered to be the main option by many researchers. Despite its benefits, its biggest drawback is the lack of software tools required for an effective design process for distributed control systems. The following work details the implementation of the IEC-61499 standard in low-cost devices using 4DIAC-FORTE for distributed control of a FESTO MPS 200 educational system, by using Unified Modeling Language (UML) diagrams as a software tool for modeling the function blocks (FBs) of the IEC-61499 standard. This work demonstrates a simple and easy way to create distributed systems.