Angel Flores-Abad

This article presents the design and implementation of a speed control system for a direct-current (DC) motor that improves the performance of an earlier design previously studied in the literature by incorporating an integral component that enhances control accuracy and robustness, as well as by including a smoother hyperbolic function in the control law to obtain a continuous, smooth, and bounded control signal. The novelty of the proposed approach lies in the integration of the integral action within a bounded hyperbolic control framework, which simultaneously ensures actuator constraint satisfaction, reduces chattering effects, and improves steady-state accuracy, making it particularly suitable for real-time implementation. A detailed description of the dynamic model of the motor is provided in both frequency-domain and state-space representations to support the control synthesis development. The control strategy was initially developed and validated using Matlab/Simulink simulations and subsequently implemented on an experimental platform, allowing a direct comparison between simulation results and experimental data for both the original and proposed controllers. A series of tests was designed to evaluate the effectiveness of the proposed controller in various operating scenarios, including constant speed regulation, disturbance rejection, stepwise speed changes, and predefined trajectory tracking. The results demonstrate that the proposed controller achieves smooth bounded control signals, improved transient response, and enhanced robustness under diverse operating conditions, confirming its effectiveness for practical DC motor speed control applications.

This paper introduces a pioneering digital engineering (DE) framework for developing space robotic systems tailored for in-space servicing, assembly, and manufacturing (ISAM) missions in a reduced-gravity environment. The research leverages digital twin principles, the Internet of Things (IoT), and cloud services to create a DE framework to support the development and testing of robotic systems to support ISAM activities. This comprehensive approach, paired with digital threads—pathways for continuous data integration across the framework—enables enhanced project lifecycle management, from design to operational analysis. Advanced IoT communication protocols create threads for real-time data exchange, which is vital for future digital twin technologies such as adaptation, distributed manufacturing, and monitoring. The proposed approach is validated in real and digital entities of exemplary robotic motion tasks in a reduced gravity scenario.

To fulfil the demands of the industry in autonomous intelligent inspection, innovative frameworks that allow Convolutional Neural Networks to run at the edge in real‐time are required. This paper proposes an end‐to‐end approach and system to enable crack detection onboard a customised embedded system. In order to make possible the deployment and execution on edge, this work develops a dataset by combining new and existing images, it introduces a quantization approach that includes inference optimization, memory reuse, and freezing layers. Real‐time, onsite results from aerial and hand‐held setup images of industrial environments show that the system is capable of identifying and localiszing cracks within the field of view of the camera with a mean average precision (mAP) of 98.44% and at ~2.5 frames per second with real‐time inference. Therefore, it is evidenced that, despite using a full model, the introduced model customization improved the mAP by ~8% with respect to lighter state‐of‐the‐art models, and the quantization technique led to a model inference two times faster. The proposed intelligent and autonomous approach advances common offline inspection techniques to enable on‐site, artificial intelligence‐based inspection systems, which also aid in reducing human errors and enhance safety conditions by automatically performing defect‐recognition in tight and difficult‐to‐reach spots.

The autonomous control of landing procedures can provide the efficiency and precision that are vital for the successful, safe completion of space operations missions. Controlling a lander with this precision is challenging because the propellants, which will be expended during the operations, represent a significant fraction of the lander’s mass. The mass variation of each tank profoundly influences the inertia and mass characteristics as thrust is generated and complicates the precise control of the lander state. This factor is a crucial consideration in our research and methodology. The dynamics model for our lander was developed where the mass, inertia, and center of mass (COM) vary with time. A feed-forward neural network (NN) is incorporated into the dynamics to capture the time-varying inertia tensor and COM. Moreover, the propellant takes time to travel through the feed lines from the storage tanks to the engine; also, the solenoid valves require time to open and close. Therefore, there are time delays between the actuator and the engine response. To take into account these sources of variations, a combined time delay is also included in the control loop to evaluate the effect of delays by fluid and mechanisms on the performance of the controller. The time delay is estimated numerically by a Computational Fluid Dynamics (CFD) model. As part of the lander’s control mechanism, a thrust vector control (TVC) with two rotational gimbals and a reaction control system (RCS) are incorporated into the dynamics. Simple proportional, integral, and derivative (PID) controllers are designed to control the thrust, the gimbal angles of the TVC, and the torque required by the RCS to manipulate the lander’s rotation and altitude. A complex mission with several numerical examples is presented to verify the hover and rotational motion control.

Safe autonomous landing for Unmanned Aerial Vehicles (UAVs) in populated areas is a crucial aspect for successful integration of UAVs in populated environments. Nonetheless, validating autonomous landing in real scenarios is a challenging task with a high risk of injuring people. In this work, we propose a framework for safe real-time and thorough evaluation of vision-based autonomous landing in populated scenarios, using photo-realistic virtual environments and physics-based simulation. The proposed evaluation pipeline includes the use of Unreal graphics engine coupled with AirSim for realistic drone simulation to evaluate landing strategies. Then, Software-/Hardware-In-The-Loop can be used to test beforehand the performance of the algorithms. The final validation stage consists in a Robot-In-The-Loop evaluation strategy where a real drone must perform autonomous landing maneuvers in real-time, with an avatar drone in a virtual environment mimicking its behavior, while the detection algorithms run in the virtual environment (virtual reality to the robot). This method determines the safe landing areas based on computer vision and convolutional neural networks to avoid colliding with people in static and dynamic scenarios. To test the robustness of the algorithms in adversary conditions, different urban-like environments were implemented, including moving agents and different weather conditions. We also propose different metrics to quantify the performance of the landing strategies, establishing a baseline for comparison with future works on this challenging task, and analyze them through several randomized iterations. The proposed approach allowed us to safely validate the autonomous landing strategies, providing an evaluation pipeline, and a benchmark for comparison. An extensive evaluation showed a 99% success rate in static scenarios and 87% in dynamic cases, demonstrating that the use of autonomous landing algorithms considerably prevents accidents involving humans, facilitating the integration of drones in human-populated spaces, which may help to unleash the full potential of drones in urban environments. Besides, this type of development helps to increase the safety of drone operations, which would advance drone flight regulations and allow their use in closer proximity to humans.

Space assets like communication satellites, space telescopes, and microgravity research stations are essential for human knowledge development. Nevertheless, space assets are subject to failure, damage, or obsolescence in their life cycle. A space asset's failure, damage, or obsolescence is typically addressed using teleoperated space robot systems to diagnose, repair, or upgrade it. In these activities, the region of interest or components to be serviced may be visually occluded by light or a physical obstacle, complicating the teleoperated maneuver and compromising the mission's success and safety as a collision may occur due to blind spots generated by visual occlusions. This paper presents an Augmented Reality (AR)-based robotic framework that allows the robot to perform an on-orbit servicing task despite visually occluded areas. It allows the user to dynamically obtain the best view in a 3D (three-dimensional) model. Besides, for the robot tool to accurately reproduce the natural motion of a human operator's hand, a direct handpresence device is used to directly map the avatar's hand motion to the robot’ send-effector(EE) motion. The system is validate din an AR environment with virtual and physical entities to repair a spacecraft's solar panel in a visually occluded area for the robot. The experimental results demonstrate that because the virtual environment can be manipulated in real-time to show the best perspective to the human operator, the repair trajectory was generated without compromising the safety and operations, even though the robot's EE and the camera-in-hand were not able to observe the area of interest directly.

Inspection of coal-fired power plants is frequently dangerous, includes difficult places to reach, and can turn expensive due to the downtimes and cost of inspection crew. Robotic systems have shown capabilities to address some of these issues, but most of the current robotic inspection technology in power plants is designed for specific components. Conversely, recent advances in machine vision have empowered aerial platforms for long-range, remotely-controlled, GPS-based inspections of industrial plants. This capability has led to wide spread utilization of aerial robots (commonly termed Drones, UVS or UAS) platforms for inspection in less challenging environments where both collision avoidance, and GPS reception are not significant issues. The challenge in adapting airborne technology for power plant inspection lies in internal structures and the complex network of piping, and distribution systems, which impose significant risks for collision and can hinder the reception and transmission of GPS signals. The current state of the art in aerial inspection technology within the energy sector is controlled via radio control, and utilizes GPS-based navigation, for inspection of large-scale plants such as offshore platforms and wind turbine parks. Nevertheless, close-range and autonomous inspection in the GPS-denied environments of power plants has not yet been achieved, as itmore » requires precise guidance and navigation with real-time situational awareness and obstacle avoidance capabilities. This endeavor introduced the use of rotary wing flying robots, due to their station keeping and vertical take-off capabilities for power plant components inspection. To enable close quarter inspection two methods were used. One method uses the 3D CAD (Three-dimensional Computer-Aided Design) model of the asset to inspect to generate the UAV’s inspection path. To acquire, analyze and process the 3D model, first, the STL file is produced to obtain surface points and vectors normal to the surface. Later, by introducing other variables such as wall offset and a controlled trajectory between each outline and each subsequent layer, the flight path is generated. The proposed framework will generate a path that will pass as close as desired from the surface and navigate in intricate environments. A second method, use advanced manufacturing techniques such as CNC (Computer Numerical Control) and additive manufacturing. Once the inspection flight path is obtained, vision-based navigation systems are employed to have the UAV autonomously tracking the provided trajectory. Finally, Artificial Intelligence-enabled developments are in charge of detecting cracks and corrosion in structural components of power plants. The proposed methods are validated in simulations, laboratory and industrial setups, where it is shown that the developed systems acting together enable close-quarter autonomous aerial inspection and mapping in power plant assets. The system can be further improved by adding more sensors to navigate in different GPS-denied environments, with non-homogeneous lighting conditions, dust and in general situations where vision-based systems may fail. « less

Automatic real-time detection of structural damages on site is a required technology to enable rapid, accurate, and on-site inspection. This paper introduces an automated intelligent inspection system capable of detecting structural problems, such as cracks, in real-time at the edge of power plant components. Since no available dataset was suitable for this case study, a real dataset was created by combining new and existing. For inspection, this project customized a Deep Neural Network (DNN) model to fit our application, including its quantization to enable deployment at the edge. Real-time, on-site results from aerial and hand-held setup images of the stack of an old power plant show that the system is capable of identifying and localizing cracks within the field of view (FOV) of the camera with a mean average precision (mAP) of 98.44% and ∼ at 2.5 frames per second (FPS) with real-time inference for crack detection at the edge.