Abstract
Nowadays, the robotic welding joint estimation, or weld seam tracking, has improved according to the new developments on computer vision technologies. Typically, the advances are focused on solving inaccurate procedures that advent from the manual positioning of the metal parts in welding workstations, especially in SMEs. Robotic arms, endowed with the appropriate perception capabilities, are a viable solution in this context, aiming for enhancing the production system agility whilst not increasing the production set-up time and costs. In this regard, this paper proposes a local perception pipeline to estimate joint welding points using small-sized/low-cost 3D cameras, following an eyes-on-hand approach. A metrological 3D camera comparison between Intel Realsene D435, D415, and ZED Mini is also discussed, proving that the proposed pipeline associated with standard commercial 3D cameras is viable for welding operations in an industrial environment.

Abstract
Industry 4.0 is changing the manufacturing paradigms across industries. However, many repetitive processes still rely heavily on human workers, as in the case of the automotive industry, where the final quality inspection of assembled vehicles is still performed using a paper-based conformity list. We instead propose a hybrid solution where a deep learning-based hierarchical autonomous detection system identifies the non-conforming parts and informs the operator via a wearable device, trained exclusively with simulated data. This scalable and cost-effective system achieved a 65.7% accuracy score, which, considering the experimental nature of this work, further confirms the potential of this approach.

Abstract

Abstract
Analysis of locomotion is often used as a measure for impairment and recovery following experimental peripheral nerve injury. Compared to rodents, sheep offer several advantages for studying peripheral nerve regeneration. In the present study, we compared for the first time, two-dimensional (2D) and three-dimensional (3D) hindlimb kinematics during obstacle avoidance in the ovine model. This study obtained kinematic data to serve as a template for an objective assessment of the ankle joint motion in future studies of common peroneal nerve (CP) injury and repair in the ovine model. The strategy used by the sheep to bring the hindlimb over a moderately high obstacle, set to 10% of its hindlimb length, was pronounced knee, ankle and metatarsophalangeal flexion when approaching and clearing the obstacle. Despite the overall time course kinematic patterns about the hip, knee, ankle, and metatarsophalangeal were identical, we found significant differences between values of the 2D and 3D joint angular motion. Our results showed that the most apparent changes that occurred during the gait cycle were for the ankle (2D-measured STANCEmax: 157 +/- 2.4 degrees vs. 3D-measured STANCEmax: 151 +/- 1.2 degrees; P<.05) and metatarsophalangeal joints (2D-measured STANCEmin: 151 +/- 2.2 degrees vs. 3D-measured STANCEmin: 162 +/- 2.2 degrees; P<.01 and 2D-measured TO: 163 +/- 4.9 degrees vs. 3D-measured TO: 177 +/- 1.4 degrees; P<.05), whereas the hip and knee joints were much less affected. Data and techniques described here are useful for an objective assessment of altered gait after CP injury and repairin an ovine model.

Abstract
Current research shows discrepancies in the gait pattern of patients with peripheral artery disease (PAD). Some studies suggest a change in gait pattern after the manifestation of claudication pain while others found patients with PAD already show a pathological gait, even before the intermittent claudication arises, and no change once the pain manifests. This exploratory research examines what change in gait pattern can be detected once claudication pain arises with the help of an accelerometer embedded in a smartphone. This study aims to contribute to the development of a process to remotely collect accelerometer data in a mobile health application, which then can be used to analyze gait pattern in patients with PAD on a larger scale. The findings of this exploratory study show that processing and analyzing accelerometer data from smartphone for gait analysis is viable and establishes a methodology for collecting and analyzing PAD patients' data. The major limitation of this study is the small sample size that do not provide the necessary reliability of the findings, about gait pattern changes. Further gait data should be collected to help understanding the gait pattern of PAD patients and build an extended dataset to be analyzed at a larger scale.

Abstract
Mobile robotics in forests is currently a hugely important topic due to the recurring appearance of forest wildfires. Thus, in-site management of forest inventory and biomass is required. To tackle this issue, this work presents a study on detection at the ground level of forest tree trunks in visible and thermal images using deep learning-based object detection methods. For this purpose, a forestry dataset composed of 2895 images was built and made publicly available. Using this dataset, five models were trained and benchmarked to detect the tree trunks. The selected models were SSD MobileNetV2, SSD Inception-v2, SSD ResNet50, SSDLite MobileDet and YOLOv4 Tiny. Promising results were obtained; for instance, YOLOv4 Tiny was the best model that achieved the highest AP (90%) and F1 score (89%). The inference time was also evaluated, for these models, on CPU and GPU. The results showed that YOLOv4 Tiny was the fastest detector running on GPU (8 ms). This work will enhance the development of vision perception systems for smarter forestry robots.