Abstract
Autonomous navigation in agriculture is very challenging as it usually takes place outdoors where there is rough terrain, uncontrolled natural lighting, constantly changing organic scenarios and sometimes the absence of a Global Navigation Satellite System (GNSS). In this work, a single camera and a Google coral dev Board Edge Tensor Processing Unit (TPU) setup is proposed to navigate among a woody crop, more specifically a vineyard. The guidance is provided by estimating the vanishing point and observing its position with respect to the central frame, and correcting the steering angle accordingly. The vanishing point is estimated by object detection using Deep Learning (DL) based Neural Networks (NN) to obtain the position of the trunks in the image. The NN's were trained using Transfer Learning (TL), which requires a smaller dataset than conventional training methods. For this purpose, a dataset with 4221 images was created considering image collection, annotation and augmentation procedures. Results show that our framework can detect the vanishing point with an average of the absolute error of 0.52. and can be considered for autonomous steering.

Abstract
Autonomous navigation in agriculture is very challenging as it usually takes place outdoors where there is rough terrain, uncontrolled natural lighting, constantly changing organic scenarios and sometimes the absence of Global Navigation Satellite System (GNSS) signal. In this work, a monocular visual system is proposed to estimate angular orientation and navigate between woody crops, more specifically a vineyard, using a Proportional Integrative Derivative (PID)-based controller. The guidance is provided by combining two ways to find the center of the vineyard: First, by estimating the vanishing point and second, by averaging the position of the two closest base trunk detections. Then, by the monocular angle perception, the angular error is determined. For obtaining the trunk position in the image, object detection using Deep Learning (DL) based Neural Networks (NN) is used. To evaluate the proposed controller, a visual vineyard simulation is created using Gazebo. The proposed joint controller is able to travel along a simulated straight vineyard with an RMS error of 1.17 cm. Moreover, a simulated curved vineyard modeled after the Douro region is tested in this work, where the robot was able to steer with an RMS error of 7.28 cm. © 2021 IEEE.

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
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
Robotics education plays a crucial role in developing STEM skills. However, university-level courses often emphasize theoretical learning, which can lead to decreased student engagement and motivation. In this paper, we tackle the challenge of providing hands-on robotics experience in higher education by adapting a mobile robot originally designed for competitions to be used in laboratory classes. Our approach integrates real-world robot operation into coursework, bridging the gap between simulation and physical implementation while maintaining accessibility. The robot's software is developed using ROS, and its effectiveness is assessed through student surveys. The results indicate that the platform increases student engagement and interest in robotics topics. Furthermore, feedback from teachers is also collected and confirmed that the platform boosts students' confidence and understanding of robotics.

Abstract
Climate change, limited natural resources, and the increase in the world's population impose society to produce food more sustainably, with lower energy and water consumption. The use of robots in agriculture is one of the most promising solutions to change the paradigm of agricultural practices. Agricultural robots should be seen as a way to make jobs easier and lighter, and also a way for people who do not have agricultural skills to produce their food. The PixelCropRobot is a low-cost, open-source robot that can perform the processes of monitoring and watering plants in small gardens. This work proposes a mission supervisor for PixelCropRobot, and general agricultural robots, and presents a prototype of user interface to this mission supervision. The communication between the mission supervisor and the other components of the system is done using ROS2 and MQTT, and mission file standardized. The mission supervisor receives a prescription map, with information about the respective mission, and decomposes them into simple tasks. An A* algorithm then defines the priority of each mission that depends on factors like water requirements, and distance travelled. This concept of mission supervisor was deployed into the PixelCropRobot and was validated in real conditions, showing a enormous potential to be extended to other agricultural robots.