Isabel García Morales

The adoption of cobots in Industry 4.0 requires training strategies accessible to users with no prior knowledge of robotics or programming. This work presents X-HRC, a virtual reality platform developed within the MASTER-XR project and integrated into VIROO, which guides users through a collaborative robotic cell, introducing them to key tasks such as defining safety zones, programming a cobot through hand-guiding, sequencing tasks using functional blocks, and acting as an operator during task execution. The platform was developed in two phases: scenario modelling and validation in Visual Components, followed by the implementation of the immersive experience in VIROO. The platform incorporates visual aids, narration, difficulty levels, and a user and metrics management panel. A preliminary usability evaluation with secondary school female students with no previous experience showed a predominantly positive perception of the platform’s realism and ease of use, supporting its potential as a tool for outreach and introductory training in human–cobot collaboration.

Robotic assistance in minimally invasive surgery has significantly improved precision and dexterity; however, many supportive tasks, such as blood aspiration, still rely on manual operation. This work presents the design and implementation of a supervised autonomous robotic aspirator for detecting and removing bleeding in an in vitro experimental model. The proposed system integrates a perception module based on a convolutional neural network for real-time blood segmentation, a task planner for high-level action execution, and a control strategy based on artificial potential fields for autonomous navigation. Additionally, a mixed-reality human–robot interaction interface is incorporated to enable system supervision and seamless transition to teleoperation when required. The system was experimentally validated with a set of in vitro experiments under three representative bleeding scenarios, evaluating four suction strategies based on the computation method for the target selection. Results demonstrate high blood removal rates (above 80% in all cases) and high suction efficiency. The comparative analysis reveals that the performance of the suction strategies is scenario-dependent and highlights a trade-off between suction efficiency and removed area. These findings support the feasibility of autonomous robotic aspiration and provide insights into the design of adaptive strategies for surgical assistance, contributing toward increased task autonomy and reduced need for continuous manual suction control during minimally invasive procedures.

Robotic assistance in minimally invasive surgery has significantly improved precision and dexterity; however, many supportive tasks, such as blood aspiration, still rely on manual operation. This work presents the design and implementation of an autonomous robotic aspirator capable of detecting and removing intraoperative bleeding without continuous human intervention. The proposed system integrates a perception module based on a convolutional neural network for real-time blood segmentation, a task planner for high-level actions execution, and a control strategy based on artificial potential fields for autonomous navigation. Additionally, a mixed-reality human–robot interaction interface is incorporated to enable system supervision and seamless transition to teleoperation when required. The system was experimentally validated with a set of in-vitro experiments under three representative bleeding scenarios, evaluating four suction strategies based on the computation method for the target selection. Results demonstrate fast reaction times (below 0.04 s) and high blood removal rates (above 80% in all cases). The comparative analysis reveals that the performance of the suction strategies is scenario-dependent and highlights a trade-off between suction efficiency and removed area. These findings support the feasibility of autonomous robotic aspiration and provide insights into the design of adaptive strategies for surgical assistance, contributing toward increased autonomy and improved workflow efficiency in minimally invasive procedures.

Laparoscopic surgery provides minimally invasive access to the abdominal cavity but poses control challenges for robotic systems due to the fulcrum constraint at the abdominal wall and the simultaneous interaction of the instrument with both the abdominal wall and internal soft tissue. While current clinical platforms (e.g., da Vinci) primarily rely on visual feedback and do not possess force sensors at the instrument tip, the transition to autonomous robotic surgery requires precise force feedback to ensure safety and effective tissue manipulation. Therefore, developing methods to decouple interaction forces using a single force sensor configuration is a critical enabling technology for future instrumented surgical robots. This paper presents a force-decoupling method that estimates, using only one force sensor, the individual forces applied to the abdominal wall and to internal soft tissue through a viscoelastic modeling approach based on Maxwell elements. Two configurations were evaluated, showing that a single-element Maxwell model provides the best trade-off between accuracy and computational complexity, achieving estimation errors of 9% and 13% for abdominal wall forces, with a root mean square error (RMSE) below 0.36 N. The method was implemented and experimentally validated in a force-controlled robotic system, demonstrating its effectiveness in improving force regulation and interaction safety without requiring additional sensors.

In minimally invasive surgery, robotics integration has been crucial, with a current focus on developing collaborative algorithms to reduce surgeons’ workload. Effective human-robot collaboration requires robots to perceive surgeons’ gestures during interventions for appropriate assistance. Research in this task has utilized both image data, mainly using Deep Learning and Convolutional Neural Networks, and kinematic data extracted from the surgeons’ instruments, processing kinematic sequences with Markov models, Recurrent Neural Networks and even unsupervised learning techniques. However, most studies that develop recognition models with kinematic data do not take into account any study of the significance that each kinematic variable plays in the recognition task, allowing for informed decisions at the time of training simpler models and choosing the sensor systems in deployment platforms. For that purpose, this work models the laparoscopic suturing manoeuvre as a set of simpler gestures to be recognized and, using the ReliefF algorithm on the JIGSAWS dataset’s kinematic data, presents a study of significance of the different kinematic variables. To validate this study, three classification models based on the multilayer perceptron and on Hidden Markov Models have been trained using both the complete set of variables and a reduced selection including only the most significant. The results show that the aperture angle and orientation of the surgical tools retain enough information about the chosen gestures that the accuracy does not vary between equivalent models by more than 5.84% in any case.

La cirugía laparoscópica es una cirugía mínimamente invasiva que hace reducidas incisiones en el tejido del paciente durante el procedimiento. Una de las tareas más desafiantes dentro de este campo es la sutura realizada con robots. Este artículo se centra en el algoritmo de sutura automatizada por medio de visión y un controlador fuerza-posición para manejar la herramienta dentro del paciente. El algoritmo recoge una primera aproximación donde la herramienta se sitúa en el punto de sutura, pero no ejecuta la entrada y posterior salida de la aguja en la piel. Se usa una plataforma robótica formada por dos brazos manipuladores, uno para manipular un instrumento quirúrgico a través del punto de fulcro y otro intra-abdominal con cámara con dos grados de libertad, para realizar los experimentos de dicho algoritmo. El artículo se compone de un algoritmo de visión para detectar heridas y localizar la ubicación de los puntos de sutura por medio de imágenes de profundidad, un módulo de navegación para generar la trayectoria de la sutura, y un controlador para ubicar la herramienta en dichos puntos sin dañar la pared abdominal del paciente. Por último, tanto el sistema de navegación como el de control se validaron mediante pruebas in vitro.

Este trabajo desarrolla la implantación de un entorno de trabajo cíber-físico para cirugía laparoscópica que permite utilizar un simulador quirúrgico con un brazo robótico, para así facilitar el aprendizaje y la investigación con este tipo de sistemas. Se propone y valida una configuración para cada uno de los elementos del quirófano que permite cumplir todas las restricciones funcionales. También se implanta un Modelo Cinemático Inverso para el brazo robótico redundante que devuelve la solución más adecuada que asegura el cumplimiento de estas restricciones. El entorno de trabajo se ha implementado haciendo uso de ROS y MATLAB, realizando una serie de pruebas a fin de validar el desarrollo de aplicaciones utilizando este framework.

Endonasal surgery is a minimally invasive approach for the removal of pituitary tumors (sarcomas). In this type of procedure, the surgeon has to complete the surgical maneuvers for sarcoma resection with extreme precision, as there are many vital structures in this area. Therefore, the use of robots for this type of intervention could increase the success of the intervention by providing accurate movements. Research has focused on the development of teleoperated robots to handle a surgical instrument, including the use of virtual fixtures to delimit the working area. This paper aims to go a step further with a platform that includes a teleoperated robot and an autonomous robot dedicated to secondary tasks. In this way, the aim is to reduce the surgeon’s workload so that he can concentrate on his main task. Thus, the article focuses on the description and implementation of a navigator that coordinates both robots via a force/position control. Finally, both the navigation and control scheme were validated by in-vitro tests.

In the last five years, deep learning has attracted great interest in computer-assisted systems for Minimally Invasive Surgery. The straightforward accessibility to images in surgical interventions makes deep neural networks enormously powerful for solving classification problems in complex surgical scenarios. The objective of this work is to provide readers a survey on deep learning models applied to minimally invasive surgery, identifying the different architectures used depending on the application, the results achieved until now, and the publicly available surgical datasets that can be used for validating new studies. A total of 85 publications have been extracted from manual research from four databases (IEEE Xplorer, Springer Link, Science Direct, and ACM Digital Library). After analyzing all these studies, they have been classified into four applications: surgical image analysis, surgical task analysis, surgical skill assessment, and automation of surgical tasks. This work provides a technical description of these works and a comparison among them. Finally, promising research directions to advance in this field are identified.

En este trabajo se presenta una iniciativa encaminada a introducir y fomentar el uso de los videotutoriales como herramienta de apoyo para el aprendizaje de los conceptos básicos del control automático en titulaciones universitarias técnicas. Los contenidos desarrollados, disponibles en el canal de YouTube denominado “Automática”, se han agrupado de acuerdo a los principales bloques conceptuales de la materia y han sido puestos a disposición del alumnado en el espacio del campus virtual de la Universidad de Málaga (UMA) asociado a 13 asignaturas impartidas por el Dpto. de Ingeniería de Sistemas y Automática de la UMA en los cursos académicos 2017/2018 y 2018/2019. Los resultados obtenidos a través de encuestas de opinión cumplimentadas por los estudiantes muestran la buena acogida de los mismos. Por otro lado, el cruce de los datos de uso (entendido como tiempo efectivo de reproducción) recogidos durante este periodo y las calificaciones obtenidas a través de pruebas de conocimiento diseñadas ad-hoc revelan la existencia de una relación entre el empleo del material desarrollado y la mejora del rendimiento académico de los estudiantes.