Abstract
The robotic grasping task persists as a modern industry problem that seeks autonomous, fast implementation, and efficient techniques. Domestic robots are also a reality demanding a delicate and accurate human-machine interaction, with precise robotic grasping and handling. From decades ago, with analytical heuristics, to recent days, with the new deep learning policies, grasping in complex scenarios is still the aim of several works' that propose distinctive approaches. In this context, this paper aims to cover recent methodologies' development and discuss them, showing state-of-the-art challenges and the gap to industrial applications deployment. Given the complexity of the related issue associated with the elaborated proposed methods, this paper formulates some fair and transparent definitions for results' assessment to provide researchers with a clear and standardised idea of the comparison between the new proposals.

Abstract
The pose control (position and orientation) of a robot is important to control how and when the robot gets to the desired pose at the desired time in order to perform some task. Controlling omnidirectional robots is of great interest due to their complete maneuverability. So, we use Proportional-Integrative (PI), Proportional-Derivative (PD), and Feed-Forward (FF) controllers to control the pose of an omnidirectional robot in space and in time. The proposed controller approximates the future trajectory (a subset of points) on parametric polynomials for computing the derivatives needed in the FF. In the simulations performed, it was analyzed the size of the future trajectory horizon for the controller depending on the robot's velocity, and the proposed controller was compared to PD-only and a generic GoToXY controller. The results demonstrated that the proposed controller achieves better results than the other two both in space and in time.

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
The development of robotic solutions for agriculture requires advanced perception capabilities that can work reliably in any crop stage. For example, to automatise the tomato harvesting process in greenhouses, the visual perception system needs to detect the tomato in any life cycle stage (flower to the ripe tomato). The state-of-the-art for visual tomato detection focuses mainly on ripe tomato, which has a distinctive colour from the background. This paper contributes with an annotated visual dataset of green and reddish tomatoes. This kind of dataset is uncommon and not available for research purposes. This will enable further developments in edge artificial intelligence for in situ and in real-time visual tomato detection required for the development of harvesting robots. Considering this dataset, five deep learning models were selected, trained and benchmarked to detect green and reddish tomatoes grown in greenhouses. Considering our robotic platform specifications, only the Single-Shot MultiBox Detector (SSD) and YOLO architectures were considered. The results proved that the system can detect green and reddish tomatoes, even those occluded by leaves. SSD MobileNet v2 had the best performance when compared against SSD Inception v2, SSD ResNet 50, SSD ResNet 101 and YOLOv4 Tiny, reaching an F1-score of 66.15%, an mAP of 51.46% and an inference time of 16.44 ms with the NVIDIA Turing Architecture platform, an NVIDIA Tesla T4, with 12 GB. YOLOv4 Tiny also had impressive results, mainly concerning inferring times of about 5 ms.

Abstract
There are several industrial processes that are controlled by a PID or similar controller. In robotics it is also usual the need of position control of joints. Tune a controller is the process to obtain the gains that optimise the behaviour of the system while maintaining its stability and robustness. This paper presents an approach of tuning a speed controller using the Internal Model Control (IMC) method and a position controller using the second order Bessel prototype while testing in different controllers methodology, such as PID, Cascade and feedforward combination with dead zone compensation. In order to compare the controllers, results for an Hermite reference position will allow to validate the proposed solution.

Abstract
This paper presents a novel light-weighted Unmanned Aerial Vehicle (UAV), an over-actuated tilt-rotor quadrotor with an innovative control allocation technique, named as Fast Control Allocation (FCA). In this arrangement, every motor has its own independent tilting command angle. By using this novel approach, the aircraft enhances its yawing capability and increases one more actuation domain: forward/backward velocity. However, this approach generates a control allocation matrix with non-unique solutions, breaking the effectiveness matrix into two parts. The first one is created considering the yawing torque and forward/backward velocity, and the second one considers all aircraft dynamics, running iteratively until the convergence criteria are reached. The results showed a well designed UAV where the FCA convergence and robustness was visible, allowing reliable and safe flight conditions with low computational effort control boards.