Abstract
The Multi-View Stereo (MVS) is a key process in the photogrammetry workflow. It is responsible for taking the camera's views and finding the maximum number of matches between the images yielding a dense point cloud of the observed scene. Since this process is based on the matching between images it greatly depends on the abil-ity of features matching throughout different images. To improve the matching performance several researchers have proposed the use of Convolutional Neural Networks (CNNs) to solve the MVS problem. Despite the progress in the MVS problem with the usage of CNNs, the Video RAM (VRAM) consumption within these approaches is usually far greater than classical methods, that rely more on RAM, which is cheaper to expand than VRAM. This work then follows the progress made in CasMVSNet in the reduction of GPU memory usage, and further study the changes in the feature extraction process. The Average Group-wise Correlation is used in the cost vol-ume generation, to reduce the number of channels in the cost volume, yielding a reduction in GPU memory usage without noticeable penalties in the result. The deformable convolutions are applied in the feature extraction net -work to augment the spatial sampling locations with learning offsets, without additional supervision, to further improve the network's ability to model transformations. The impact of these changes is measured using quanti-tative and qualitative tests using the DTU and the Tanks and Temples datasets. The modifications reduced the GPU memory usage by 32% and improved the completeness by 9% with a penalty of 6.6% in accuracy on the DTU dataset.(c) 2021 Published by Elsevier B.V.

Abstract
The Agri-Food production requirements needs a more efficient and autonomous processes, and robotics will play a significant role in this process. Deploying agricultural robots on the farm is still a challenging task. Particularly in slope terrains, where it is crucial to avoid obstacles and dangerous steep slope zones. Path planning solutions may fail under several circumstances, as the appearance of a new obstacle. This work proposes a novel open-source solution called AgRobPP-CA to autonomously perform obstacle avoidance during robot navigation. AgRobPP-CA works in real-time for local obstacle avoidance, allowing small deviations, avoiding unexpected obstacles or dangerous steep slope zones, which could impose a fall of the robot. Our results demonstrated that AgRobPP-CA is capable of avoiding obstacles and high slopes in different vineyard scenarios, with low computation requirements. For example, in the last trial, AgRobPP-CA avoided a steep ramp that could impose a fall to the robot.

Abstract
Purpose The motivation for robotics research in the agricultural field has sparked in consequence of the increasing world population and decreasing agricultural labor availability. This paper aims to analyze the state of the art of pruning and harvesting manipulators used in agriculture. Design/methodology/approach A research was performed on papers that corresponded to specific keywords. Ten papers were selected based on a set of attributes that made them adequate for review. Findings The pruning manipulators were used in two different scenarios: grapevines and apple trees. These manipulators showed that a light-controlled environment could reduce visual errors and that prismatic joints on the manipulator are advantageous to obtain a higher reach. The harvesting manipulators were used for three types of fruits: strawberries, tomatoes and apples. These manipulators revealed that different kinematic configurations are required for different kinds of end-effectors, as some of these tools only require movement in the horizontal axis and others are required to reach the target with a broad range of orientations. Originality/value This work serves to reduce the gap in the literature regarding agricultural manipulators and will support new developments of novel solutions related to agricultural robotic grasping and manipulation.

Abstract
The iHandU system is a wearable device that quantitatively evaluates changes in wrist rigidity during Deep Brain Stimulation (DBS) surgery, allowing clinicians to find optimal stimulation settings that reduce patient symptoms. Robotic accuracy is also especially relevant in DBS surgery, as accurate electrode placement is required to increase effectiveness and reduce side effects. The main goal of this work is to integrate the advantages of each system in a closed-loop system between an industrial robot and the iHandU system. For this purpose, a comparative analysis of a Leksell stereotactic frame and neuro-robotic system accuracies was performed using a lab-made phantom. The neuro-robotic system reached 90% of trajectories, while the stereotactic frame reached all trajectories. There are significant differences in accuracy errors between these trajectories (p < 0.0001), which can be explained by the high correlation between the neuro-robotic system errors and the distance from the trajectory to the origin of the Leksell coordinate system (?=0.72). Overall accuracy is comparable to existing neuro-robotic systems, achieving a deviation of (1.0 ± 0.5) mm at the target point. The accuracy of DBS electrode positioning and stimulation parameters choice leads to better long-term clinical outcomes in Parkinson's disease patients. Our neuro-robotic system combines real-time feedback assessment of the patient's symptomatic response and automatic positioning of the DBS electrode in a specific brain area.

Abstract
In Industry 4.0 and Agriculture 4.0, there are logistics areas where robots can play an important role, for example by following a person at a certain distance. These robots can transport heavy tools or simply help collect certain items, such as harvested fruits. The use of Ultra Wide Band (UWB) transceivers as range sensors is becoming very common in the field of robotics, i.e. for localising goods and machines. Since UWB technology has very accurate time resolution, it is advantageous for techniques such as Time Of Arrival (TOA), which can estimate distance by measuring the time between message frames. In this work, UWB transceivers are used as range sensors to track pedestrians/operators. In this work we propose the use of two algorithms for relative localization, between a person and robot. Both algorithms use a similar 2dimensional occupancy grid, but differ in filtering. The first is based on a Extended Kalman Filter (EKF) that fuses the range sensor with odometry. The second is based on an Histogram Filter that calculates the pedestrian position by discretizing the state space in well-defined regions. Finally, a controller is implemented to autonomously command the robot. Both approaches are tested and compared on a real differential drive robot. Both proposed solutions are able to follow a pedestrian at speeds of 0.1m/s, and are promising solutions to complement other solutions based on cameras and LiDAR.

Abstract
Analytical grade 'in vivo' plant metabolic quantification using spectroscopy is a key enabling technology for precision agriculture.Current methods such as PLS, ANN and LS-SVM are non-optimal for resolving spectral interference and matrix effects to provide similar results to the analytical chemistry laboratory. This research presents a new self-learning artificial intelligence (SL-AI) method based on the search of covariance modes. These isolate the different modes of interference present in spectral data, allowing the consistent quantification of constituents. A review of the state-of-the-art methods with the figures of merit mean absolute standard error percentage (MASEP) and Pearson correlation coefficient (R) is presented for comparison and discussion. 707 grapes were quantified for glucose, fructose, malic and tartaric acids in five wine-making and one table grape varieties, and used to benchmark the new method against the state-of-the-art methodologies: partial least squares, local partial least squares, artificial neural networks and least squares support vector machines. SL-AI provides consistent quantifications, whereas previous methods exhibit data-driven performance dependence. Pearson correlations of 0.93 to 0.99 and MASEP of 3.70% to 7.33% were obtained with the new methodology. Local partial least squares, the method with the best benchmarks from literature, achieved correlations of 0.81 to 0.94 and MASEP of 8.00% to 13.4%. The covariance mode isolates a particular interference, providing a direct relationship between spectral inference and constituent concentrations, consistent with the Beer-Lambert law. Such quantifies non-dominant absorbance constituents (e.g. sugars and acids), which is a significant step towards 'in vivo' plant physiology-based precision agriculture.