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.

Abstract
Nowadays, robotic manipulators’ uses are broader than industrial needs. They are applied to perform agricultural tasks, consumer services, medical surgeries, among others. The development of new cost-effective robotic arms assumes a prominent position to enable their wide-spread adoption in these application areas. Bearing these ideas in mind, the objective of this paper is twofold. First, introduce the hardware and software architecture and position-control design for a four Degree of Freedom (DoF) manipulator constituted by high-resolution stepper motors and incremental encoders and a cost-effective price. Secondly, to describe the mitigation strategies adopted to lead with the manipulator’s position using incremental encoders during startup and operating modes. The described solution has a maximum circular workspace of 0.7 m and a maximum payload of 3 kg. The developed architecture was tested, inducing the manipulator to perform a square path. Tests prove an accumulative error of 12.4 mm. All the developed code for firmware and ROS drivers was made publicly available. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Abstract

Abstract
Augmented and virtual reality have been experiencing rapid growth in recent years, but there is still no deep knowledge regarding their capabilities and in what fields they could be explored. In that sense, this paper presents a study on the accuracy and repeatability of Microsoft's HoloLens 2 (augmented reality device) and HTC Vive (virtual reality device) using an OptiTrack system as ground truth. For the HoloLens 2, the method used was hand tracking, whereas, in HTC Vive, the object tracked was the system's hand controller. A series of tests in different scenarios and situations were performed to explore what could influence the measures. The HTC Vive obtained results in the millimeter range, while the HoloLens 2 revealed not very accurate measurements (around 2 cm). Although the difference can seem to be considerable, the fact that HoloLens 2 was tracking the user's hand and not the system's controller made a huge impact. The results are considered a significant step for the ongoing project of developing a human-robot interface by demonstrating an industrial robot using extended reality, which shows great potential to succeed based on our data.

Abstract
The automation of agricultural processes is expected to positively impact the environment by reducing waste and increasing food security, maximising resource use. Precision spraying is a method used to reduce the losses during pesticides application, reducing chemical residues in the soil. In this work, we developed a smart and novel electric sprayer that can be assembled on a robot. The sprayer has a crop perception system that calculates the leaf density based on a support vector machine (SVM) classifier using image histograms (local binary pattern (LBP), vegetation index, average, and hue). This density can then be used as a reference value to feed a controller that determines the air flow, the water rate, and the water density of the sprayer. This perception system was developed and tested with a created dataset available to the scientific community and represents a significant contribution. The results of the leaf density classifier show an accuracy score that varies between 80% and 85%. The conducted tests prove that the solution has the potential to increase the spraying accuracy and precision.

Abstract
The world is living the fourth industrial revolution, marked by the increasing intelligence and automation of manufacturing systems. Nevertheless, there are types of tasks that are too complex or too expensive to be fully automated, it would be more efficient if the machines were able to work with the human, not only by sharing the same workspace but also as useful collaborators. A possible solution to that problem is on human-robot interaction systems, understanding the applications where they can be helpful to implement and what are the challenges they face. This work proposes the development of an industrial prototype of a human-machine interaction system through Augmented Reality, in which the objective is to enable an industrial operator without any programming experience to program a robot. The system itself is divided into two different parts: the tracking system, which records the operator's hand movement, and the translator system, which writes the program to be sent to the robot that will execute the task. To demonstrate the concept, the user drew geometric figures, and the robot was able to replicate the operator's path recorded.