Roemi Fernández

Combination of RGB and Multispectral Imagery for Discrimination of Cabernet Sauvignon Grapevine Elements

This paper proposes a sequential masking algorithm based on the K-means method that combines RGB and multispectral imagery for discrimination of Cabernet Sauvignon grapevine elements in unstructured natural environments, without placing any screen behind the canopy and without any previous preparation of the vineyard. In this way, image pixels are classified into five clusters corresponding to leaves, stems, branches, fruit and background. A custom-made sensory rig that integrates a CCD camera and a servo-controlled filter wheel has been specially designed and manufactured for the acquisition of images during the experimental stage. The proposed algorithm is extremely simple, efficient, and provides a satisfactory rate of classification success. All these features turn out the proposed algorithm into an appropriate candidate to be employed in numerous tasks of the precision viticulture, such as yield estimation, water and nutrients needs estimation, spraying and harvesting.

Multisensory system for fruit harvesting robots. Experimental testing in natural scenarios and with different kinds of crops

The motivation of this research was to explore the feasibility of detecting and locating fruits from different kinds of crops in natural scenarios. To this end, a unique, modular and easily adaptable multisensory system and a set of associated pre-processing algorithms are proposed. The offered multisensory rig combines a high resolution colour camera and a multispectral system for the detection of fruits, as well as for the discrimination of the different elements of the plants, and a Time-Of-Flight (TOF) camera that provides fast acquisition of distances enabling the localisation of the targets in the coordinate space. A controlled lighting system completes the set-up, increasing its flexibility for being used in different working conditions. The pre-processing algorithms designed for the proposed multisensory system include a pixel-based classification algorithm that labels areas of interest that belong to fruits and a registration algorithm that combines the results of the aforementioned classification algorithm with the data provided by the TOF camera for the 3D reconstruction of the desired regions. Several experimental tests have been carried out in outdoors conditions in order to validate the capabilities of the proposed system.

State of the art in robots used in minimally invasive surgeries. Natural Orifice Transluminal Surgery (NOTES) as a particular case

Purpose – This paper aims to provide an insight into recent advancements and developments of robotics for Natural Orifice Transluminal Surgery (NOTES) procedures. Design/methodology/approach – Following an introduction that highlights the evolution from Minimally Invasive Surgery (MIS) to NOTES in the medical field, this paper reviews the main robotics systems that have been designed and implemented for MIS and NOTES, summarising their advantages and limitations and remarking the technological challenges and the requirements that still should be addressed and fulfilled. Findings – The state-of-the-art presented in this paper shows that the majority of the platforms created for NOTES are laboratory prototypes, and their performances are still far from being optimal. New solutions are required to solve the problems confronted by the proposed systems such as the limited number of DOFs, the limited resolution, the optimal fixation and stiffening of the instruments for enabling stable and precise operation, th...

A New Device to Automate the Monitoring of Critical Patients’ Urine Output

Urine output (UO) is usually measured manually each hour in acutely ill patients. This task consumes a substantial amount of time. Furthermore, in the literature there is evidence that more frequent (minute-by-minute) UO measurement could impact clinical decision making and improve patient outcomes. However, it is not feasible to manually take minute-by-minute UO measurements. A device capable of automatically monitoring UO could save precious time of the healthcare staff and improve patient outcomes through a more precise and continuous monitoring of this parameter. This paper presents a device capable of automatically monitoring UO. It provides minute by minute measures and it can generate alarms that warn of deviations from therapeutic goals. It uses a capacitive sensor for the measurement of the UO collected within a rigid container. When the container is full, it automatically empties without requiring any internal or external power supply or any intervention by the nursing staff. In vitro tests have been conducted to verify the proper operation and accuracy in the measures of the device. These tests confirm the viability of the device to automate the monitoring of UO.

An Automatic Critical Care Urine Meter

Nowadays patients admitted to critical care units have most of their physiological parameters measured automatically by sophisticated commercial monitoring devices. More often than not, these devices supervise whether the values of the parameters they measure lie within a pre-established range, and issue warning of deviations from this range by triggering alarms. The automation of measuring and supervising tasks not only discharges the healthcare staff of a considerable workload but also avoids human errors in these repetitive and monotonous tasks. Arguably, the most relevant physiological parameter that is still measured and supervised manually by critical care unit staff is urine output (UO). In this paper we present a patent-pending device that provides continuous and accurate measurements of patients UO. The device uses capacitive sensors to take continuous measurements of the height of the column of liquid accumulated in two chambers that make up a plastic container. The first chamber, where the urine inputs, has a small volume. Once it has been filled it overflows into a second bigger chamber. The first chamber provides accurate UO measures of patients whose UO has to be closely supervised, while the second one avoids the need for frequent interventions by the nursing staff to empty the container.

A New Approach for Combining Time-of-Flight and RGB Cameras Based on Depth-Dependent Planar Projective Transformations

Image registration for sensor fusion is a valuable technique to acquire 3D and colour information for a scene. Nevertheless, this process normally relies on feature-matching techniques, which is a drawback for combining sensors that are not able to deliver common features. The combination of ToF and RGB cameras is an instance that problem. Typically, the fusion of these sensors is based on the extrinsic parameter computation of the coordinate transformation between the two cameras. This leads to a loss of colour information because of the low resolution of the ToF camera, and sophisticated algorithms are required to minimize this issue. This work proposes a method for sensor registration with non-common features and that avoids the loss of colour information. The depth information is used as a virtual feature for estimating a depth-dependent homography lookup table (Hlut). The homographies are computed within sets of ground control points of 104 images. Since the distance from the control points to the ToF camera are known, the working distance of each element on the Hlut is estimated. Finally, two series of experimental tests have been carried out in order to validate the capabilities of the proposed method.

A research review on clinical needs, technical requirements, and normativity in the design of surgical robots

Nowadays robots play an important role in society, mainly due to the significant benefits they provide when utilized for assisting human beings in the execution of dangerous or repetitive tasks. Medicine is one of the fields in which robots are gaining greater use and development, especially those employed in minimally invasive surgery (MIS). However, due to the particular conditions of the human body where robots have to act, the design of these systems is complex, not only from a technical point of view, but also because the clinical needs and the normativity aspects are important considerations that have to be taken into account in order to achieve better performances and more secure systems for patients and surgeons. Thus, this paper explores the clinical needs and the technical requirements that will trace the roadmap for the next scientific and technological advances in the field of robotic surgery, the metrics that should be defined for safe technology development and the standards that are being elaborated for boosting the industry and facilitating systems integration.

Reconfiguration of a Climbing Robot in an All-Terrain Hexapod Robot

This work presents the reconfiguration from a previous climbing robot to an all-terrain robot for applications in outdoor environments. The original robot is a six-legged climbing robot for high payloads. This robot has used special electromagnetic feet in order to support itself on vertical ferromagnetic walls to carry out specific tasks. The reconfigured all-terrain hexapod robot will be able to perform different applications on the ground, for example, as inspection platform for humanitarian demining tasks. In this case, the reconfigured hexapod robot will load a scanning manipulator arm with a specific metal detector as end-effector. With the implementation of the scanning manipulator on the hexapod robot, several tasks about search and localisation of antipersonnel mines would be carried out. The robot legs have a SCARA configuration, which allows low energy consumption when the robot performs trajectories on a quasi-flat terrain.

VIS-NIR, SWIR and LWIR Imagery for Estimation of Ground Bearing Capacity

Ground bearing capacity has become a relevant concept for site-specific management that aims to protect soil from the compaction and the rutting produced by the indiscriminate use of agricultural and forestry machines. Nevertheless, commonly known techniques for its estimation are cumbersome and time-consuming. In order to alleviate these difficulties, this paper introduces an innovative sensory system based on Visible-Near InfraRed (VIS-NIR), Short-Wave InfraRed (SWIR) and Long-Wave InfraRed (LWIR) imagery and a sequential algorithm that combines a registration procedure, a multi-class SVM classifier, a K-means clustering and a linear regression for estimating the ground bearing capacity. To evaluate the feasibility and capabilities of the presented approach, several experimental tests were carried out in a sandy-loam terrain. The proposed solution offers notable benefits such as its non-invasiveness to the soil, its spatial coverage without the need for exhaustive manual measurements and its real time operation. Therefore, it can be very useful in decision making processes that tend to reduce ground damage during agricultural and forestry operations.

An add-on solution to take measures automatically from critical care urine meters

Critical care patients have most of their physiological parameters automatically sensed by sophisticated commercial monitoring devices. These devices can also supervise whether the values of each parameter lie within a preestablished normal range, and warn of deviations from this range by triggering audible alarms. Automation of the sensing and supervision of physiological parameters discharges the healthcare staff of a considerable workload. Furthermore, it avoids human errors, which are common in repetitive and monotonous tasks. However, there is still a very relevant physiological parameter that is sensed and supervised manually by the healthcare staff: urine output. In this paper we present a patent-pending addon solution that can be easily incorporated into commercial urine meters to transform these manual measuring devices into automated measuring devices. The solution is based on capacitive sensors capable of taking continuous measurements of the height of the column of liquid accumulated in the various chambers that make up a commercial urine meter. An electronic unit sends the measures of the capacitive sensors via Bluetooth to a PC which calculates the urine output from this information and supervises the achievement of therapeutic goals.