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Robotic handling of objects is not always a trivial assignment, even in teleoperation where, in most cases, this might lead to stressful labor for operators. To reduce the task difficulty, supervised motions could be performed in safe scenarios to reduce the workload in these non-critical steps by using machine learning and computer vision techniques. This paper describes a novel grasping strategy based on a groundbreaking geometrical analysis which extracts diametrically opposite points taking into account surface smoothing (even those target objects that might conform highly complex shapes) to guarantee the uniformity of the grasping. It uses a monocular camera, as we are often facing space restrictions that generate the need to use laparoscopic cameras integrated in the tools, to recognize and isolate targets from the background, estimating their spatial coordinates and providing the best possible stable grasping points for both feature and featureless objects. It copes with reflections and shadows produced by light sources (which require extra effort to extract their geometrical properties) in unstructured facilities such as nuclear power plants or particle accelerators on scientific equipment. Based on the experimental results, utilizing a specialized dataset improved the detection of metallic objects in low-contrast environments, resulting in the successful application of the algorithm with error rates in the scale of millimeters in the majority of repeatability and accuracy tests.

Robotic interventions in hazardous scenarios need to pay special attention to safety, as in most cases it is necessary to have an expert operator in the loop. Moreover, the use of a multi-modal Human-Robot Interface allows the user to interact with the robot using manual control in critical steps, as well as semi-autonomous behaviours in more secure scenarios, by using, for example, object tracking and recognition techniques. This paper describes a novel vision system to track and estimate the depth of metallic targets for robotic interventions. The system has been designed for on-hand monocular cameras, focusing on solving lack of visibility and partial occlusions. This solution has been validated during real interventions at the Centre for Nuclear Research (CERN) accelerator facilities, achieving 95% success in autonomous mode and 100% in a supervised manner. The system increases the safety and efficiency of the robotic operations, reducing the cognitive fatigue of the operator during non-critical mission phases. The integration of such an assistance system is especially important when facing complex (or repetitive) tasks, in order to reduce the work load and accumulated stress of the operator, enhancing the performance and safety of the mission.

[Abstract] When a robotic intervention is required in hazardous facilities (e.g. particle accelerators or nuclear plants), it is commonly not possible to test the operation on-site in advance - a considerable challenge since robotic interventions usually require specific tasks for each location -, precluding the team from demonstrating the feasibility of the operation. It becomes mandatory to develop a particular mock-up for each operation, unsuitable for reusing it in future missions. To solve this problem, a general dynamic mock-up bench was designed, allowing to centre the testing of all remote handled tasks and to choose the best set of robots to perform them.

Resumen] En este documento se expone un sistema adaptativo y semiautónomo, el cual genera una interfaz de alto nivel para facilitar el trabajo de supervisión humana, capaz de detectar un objeto de interés, con la finalidad de trackear su posición y guiar a un Remote Operated Vehicle (ROV) haciendo uso de una cámara pan-tilt submarina, mediante la utilización de herramientas conocidas de visión por computador, y a través del middleware Robot Operating System (ROS). El trabajo se ha llevado a cabo en el contexto del proyecto coordinado MERBOTS (www.irs.uji.es/merbots), para mejorar la realimentación visual en intervenciones reales submarinas.