Space robots have been proposed to perform several tasks in-orbit such as repairing, refueling, and assembly. All those activities require the robot to interact with external objects in a safe and compliance form to avoid high impact forces that may damage the robot's components or the object to manipulate. Impedance controllers have demonstrated to be a suitable approach to perform similar tasks in ground. Therefore, implementing and analyzing impedance-based techniques in space is also worthy of study. Not only that, but also, methods that can reduce the number of components, weight and complexity should be considered. This paper proposes the use of the disturbance observer for the design of an impedance controller to perform a safe capture of a target satellite by a space robot. First, the observer aids in the determination of the contact force between the servicer and the target. Later, once the almost perfect match between the actual and estimated contact force is demonstrated, the estimated force is used as input for an impedance controller with trajectory tracking. The advantages of estimating the contact force instead of measuring it are also discussed in the document. The design of the impedance control is performed such that the capturing process is compliant and safe, and thus it can help to advance towards an autonomous capture of space objects. A simulation experiment was set up to evaluate the performance of the proposed method. The results demonstrate that the contact force is bounded and behaves smoothly in a spring-mass-damper-like manner, imposing a stable contact between the robot's end-effector and the servicer's capturing spot.

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.

SUMMARY: Robotic systems have shown capabilities to perform inspection tasks in dangerous and difficult-to-access environments, such as those found in different components of power plants. However, most of the current robotic inspection technology is designed for specific components. Aerial robots, commonly termed as Drones, have raised an option to inspect a wider range of structural components. Nevertheless, current aerial inspecting technology still relies on a human pilot with limited line of sight, field of view and a reduced perception as the drone flies away, which prevents performing close-quarter inspection in intricate, structurally complex and GPS-denied environments. This work introduces offline inspection path generation methods based on robotics-integrated to manufacturing techniques. One method uses computer-aided manufacturing (CAM) techniques and the other an additive manufacturing (AM) approach to generate the flight path. That is to say, the drone would fly along the path described by a 3D (Three Dimensional) printer’s extruder or a CNC (Computer Numerical Control) machining tool, enabling to fly very close the structure even in physical structures with a complex geometry. Once the trajectories are generated, they are introduced for its validation in the Gazebo robotics simulator. Simulation results demonstrate the proper performance of the method and confirm that this approach can be used for close inspection of structuralcomponents.

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.