This paper presents an obstacle avoidance technique for a mobile robot in human-robot collaborative (HRC) tasks. The proposed solution uses fuzzy logic rules and a convolutional neural network (CNN) in an integrated approach to detect objects during vehicle movement. The goal is to improve the robot’s navigation autonomously and ensure the safety of people and equipment in dynamic environments. Using this technique, it is possible to provide important references to the robot’s internal control system, guiding it to continuously adjust its velocity and yaw in order to avoid obstacles (humans and moving objects) while following the path planned for its task. The approach aims to improve operational safety without compromising productivity, addressing critical challenges in collaborative robotics. The system was tested in a simulated environment using the Robot Operating System (ROS) and Gazebo to demonstrate the effectiveness of navigation and obstacle avoidance. The results obtained with the application of the proposed technique indicate that the framework allows real-time adaptation and safe interaction between robot and obstacles in complex and changing industrial workspaces.

This work proposes a mathematical solution for the attitude control problem of an ornithopter wing. An ornithopter is an artificial bird or insect-like aerial vehicle whose flight and lift movements are produced and maintained by flapping wings. The aerodynamical drag forces responsible for the flying movements are generated by the wing attitude and torques applied to its joints. This mechanical system represents a challenging problem because its dynamics consist of MIMO nonlinear equations with couplings in the input variables. For dealing with such a mathematical model, an Active Disturbance Rejection Control-based (ADRC) method is considered. The cited control technique has been studied for almost two decades and its main characteristics are the use of an extended state observer to estimate the nonmeasurable signals of the plant and a state-feedback control law in standard form fed by that observer. However, even today, the application of the basic methodology requires the exact knowledge of the plant’s control gain which is difficult to measure in the case of systems with uncertain parameters. In addition, most of the related works apply the ADRC strategy to Single Input Single Output (SISO) plants. For MIMO systems, the control gain is represented by a square matrix of general entries but most of the reported works consider the simplified case of uncoupled inputs, in which a diagonal matrix is assumed. In this paper, an extension of the ADRC SISO strategy for MIMO systems is proposed. By adopting such a control methodology, the resulting closed-loop scheme exhibits some key advantages: (i) it is robust to parametric uncertainties; (ii) it can compensate for external disturbances and unmodeled dynamics; (iii) even for nonlinear plants, mathematical analysis using Laplace’s approach can be always used; and (iv) it can deal with system’s coupled input variables. A complete mathematical model for the dynamics of the ornithopter wing system is presented. The efficiency of the proposed control is analyzed mathematically, discussed, and illustrated via simulation results of its application in the attitude control of ornithopter wings.

This work describes the application of a disturbance rejection technique for controlling ball position in an air levitation experiment. The prototype consists of a light-mass ball that moves freely inside a vertical transparent cylindrical tube. The control objective is to force the ball position to track a desired vertical trajectory by defining a voltage control law for an air blower actuator. The system’s nonlinear mathematical model is presented and discussed. To deal with model uncertainties, nonlinearities and non measurable states, the design and the analysis of a Modified-Plant Active Disturbance Rejection Controller (MP-ADRC) are performed. ADRC-based controllers have been studied for decades, and their main characteristics are the use of an extended state observer (ESO) to estimate the non-measurable signals of the plant and a state-feedback control law in standard form fed by that observer. The key advantage of the adopted variant MP-ADRC, which contrasts with the basic methodology, is that it only requires knowledge of the sign $(+/-)$ of plant’s control coefficient, rather than its exact value. This improves the well-known robustness property of the method, allowing its application to a general class of dynamical systems with uncertain parameters. By adopting and using such a control methodology, the resulting closed-loop scheme exhibits several advantageous characteristics: (i) it is robust to parametric uncertainties; (ii) it can compensate for external disturbances and unmodeled dynamics; (iii) it can deal with plants with slowly time-varying control coefficient; (iv) even for nonlinear plants, mathematical analysis using Laplace transform can always be performed. This work also presents and discusses simulation and experimental results obtained with the proposed control strategy applied to a real air levitation prototype.