Obstacle avoidance is a challenging task in robot navigation, as it requires efficient and reliable methods to avoid collision and reach the desired goal. Artificial potential field methods are widely used for this purpose, as they are efficient, effective, and easy to implement. However, they are limited by the use of only one global attractor at the goal. This paper introduces and evaluates experimentally a novel technique that enhances the artificial potential field method with local attractors. Local attractors can be positioned around the obstacle so as to guide the robot detouring through preferred regions. Thus, the side the robot will pass by can be determined in advance, making the collision-free path predictable. The technique is formulated by modelling local attractors as optimal inflections, i.e., regions that do not show local minima, which coexist with the potential field generated by the obstacle and the global attractor. The method is validated using a laboratory setup that employs a camera and markers to track the poses of the robot, the obstacle, and the target. A series of experiments are conducted to examine the effect of the local attractor under different test conditions, obtained by varying the obstacle pose, the attraction intensity, and the robot velocity. The experimental results demonstrate the effectiveness of the proposed technique and highlight the aspects that require further investigation for its improvement and application.

Summary: Underground gas storage (UGS) wells are essential components in energy security. However, UGS wells present a complicated and delicate combination of elements where ensuring safe and secure functionality over long periods is paramount. This paper showcases how a digital twin is used to evaluate and forecast the link between leaks and temperature and pressure trends in a UGS well, allowing the identification and quantification of defects and, subsequently, well barrier integrity. The digital twin used for this application presents advantages compared with other solutions present on the market with regard to the simplified configuration; that is, with minimal input data, the system can produce an accurate and useful output, which is then used in the well integrity decision-making process. UGS wells present additional criticalities with respect to normal production wells due to their longer life span and the repetitive production and injection cycles. This makes early and accurate leak detection essential for the safe management of the well barriers. The proposed digital twin simulates the trends of pressure and temperature within each annulus and compares results with data from the field, allowing the identification of the position and size of leaks. A genetic algorithm is applied to optimize the placement of leaks on their specific barriers. Once a leak is identified, a risk assessment is conducted to evaluate the overall integrity of the well. If the status of the well is found to be critical enough, an intervention may be planned. The studies presented show how the digital twin has been used on two wells with similar problems. At first, it has confirmed the necessity to put the well out of service as opposed to planning maintenance, thereby saving both time and cost. In the second case, it allowed the validation of a solution that led to a 60% reduction in failure consequence, allowing the well to continue operating without major costs or risks. The errors of the resulting simulations were always confined within the 0.5 bar limit highlighting its accuracy. The system has been in use for over a year and has shown great potential in accurate and efficient identification of leaks. This has accelerated the process of well integrity evaluation and allowed timely interventions on wells that required it. On the other hand, the process has highlighted cases where previous assumptions about leak location and size were corrected using the digital twin, therefore reducing the costs of interventions. Finally, the model showcases a clear readiness for predictive capabilities aimed to select, plan, and design fit for purpose mitigating actions.

When studying complex mechatronic systems, it is useful to build models able to simulate both the dynamics of the phenomenon and the control system applied. Typically, the bodies involved are modeled as rigid bodies. In this work, a technique for modeling flexible bodies in Simulink environment is presented. Simulink is a powerful instrument where it is quite easy to integrate control algorithms with complex systems. The solution developed is presented and applied to a machining center. Modern machining centers ensure a level of accuracy that traditional manual machines cannot reach. Simulations of the working process considering vibrations are needed to obtain high precision machining. These simulations aim to determine the error in the position of the tool and to help designers in finding the optimal solution in terms of machining velocity and precision. This work is focused on the carriage of a machine tool moving along horizontal guides, typically named Z-axis. The axis is actuated and borne by a linear motor; therefore, movable constraints must be modeled. A finite-element (FE) model of the carriage was reduced with a Craig-Bampton reduction to provide the mass and stiffness matrices for an in-house Matlab simulation code. The rigid constraints of the carriage were implemented in the model as moving stiffnesses, and their value was set to obtain continuity of the constraints in the discrete model. In the end, a map of different vibrational configurations is proposed to visualize the possible errors that a machining process can generate.

Gaze tracking is gaining relevance in collaborative robotics as a means to enhance human–machine interaction by enabling intuitive and non-verbal communication. This study explores the integration of human gaze into collaborative robotics by demonstrating the possibility of controlling a robotic manipulator with a practical and non-intrusive setup made up of a vision system and gaze-tracking software. After presenting a comparison between the major available systems on the market, OpenFace 2.0 was selected as the primary gaze-tracking software and integrated with a UR5 collaborative robot through a MATLAB-based control framework. Validation was conducted through real-world experiments, analyzing the effects of raw and filtered gaze data on system accuracy and responsiveness. The results indicate that gaze tracking can effectively guide robot motion, though signal processing significantly impacts responsiveness and control precision. This work establishes a foundation for future research on gaze-assisted robotic control, highlighting its potential benefits and challenges in enhancing human–robot collaboration.

The spherical shape is an interesting approach to develop exploration robots, or rovers, thanks to its capability of ensuring omnidirectional motion and of being basically unsensitive to possible rollovers. This works intends to propose a novel detailed design for such a kind of robot and to discuss the performance that can be reached by adopting this solution. The work hence introduces the requirements assumed for the design of the robot and discloses the general layout that was selected, which includes a pendulum for motion transmission and two coupled gyroscopes to overcome high, steep obstacles, such as steps. The paper then summarizes the functional design computation carried out to size and selects the components of the system. Eventually, a control algorithm is described and tested on a complete multibody model of the robot. The results in the execution of standard maneuvers such as motion on a horizontal plane, as well as in the overcome of a step, are shown. The energetic balance of the rover is described, and some preliminary consideration about mission planning are reported in the final discussion.