Abstract
Computational thinking is the thinking process involved in formulating problems to admit a computational solution. This article describes a study in which the code.org platform was used to develop computational thinking with Elementary school students. After proper introduction and contextualization, we describe the 198 students from 4th grade involved in the study, following the process of collecting and analyzing data from the code.org platform. We conclude with the evaluation carried out by the students. The main conclusion of this study is that code.org is a valid option for developing computational thinking with Elementary school students. Also, a reliable way for students to start solving real-life problems, stimulating the capacity for abstraction through simulated and experienced practice.

Abstract
The Internet of Things asserts that several applications, such as smart cities or intelligent agriculture, can be based on various embedded systems programmed to do different tasks, by transferring data over a network from sensors to a server, where the information is stored and treated, supporting the decision-making process. In this context, LoRaWAN is an accurate network topology based on a wireless technology called LoRa that is capable of transmitting small data rates at a long range, using low-powered devices, making it ideal for the acquisition of climate variables, such as temperature and relative humidity. Applying this architecture to agriculture buildings can be very useful to guarantee indoor thermal comfort conditions. In this study, this technology is applied to a passive solar system composed by a high thermal inertia wall, defined as Trombe wall, with air vents provided in the massive wall to improve heat transfer by air convection, and an external shading device to avoid overheating during summer and heat losses during winter. It is intended to analyze the possibility to control the interiortemperature of a poultry brooding house given that, in the early stages of life, chickens need accurate climate conditions in order to enhance their growth and reduce their mortality rate. In brief, temperature values acquired by different sensors placed on the Trombe wall travel through a LoRaWAN wireless network and are received by an application that controls the actuators, in this case, the opening and closing of the Trombe wall air vents, while the external shading device is controlled locally.

Abstract
Task Scheduling assumes an integral topic in the efficiency of multiple mobile robots systems and is a key part in most modern manufacturing systems. Advances in the field of combinatorial optimisation have allowed the implementation of algorithms capable of solving the different variants of the vehicle routing problem in relation to different objectives. However few of this approaches are capable of taking into account the nuances associated with the coordinated path planning in multi-AGV systems. This paper presents a new study about the implementation of the Simulated Annealing algorithm to minimise the time and distance cost of executing a tasks set while taking into account possible pathing conflicts that may occur during the execution of the referred tasks. This implementation uses an estimation of the planned paths for the robots, provided by the Time Enhanced A* (TEA*) to determine where possible pathing conflicts occur and uses the Simulated Annealing algorithm to optimise the attribution of tasks to each robot, in order to minimise the pathing conflicts. Results are presented that validate the efficiency of this algorithm and compare it to an approach that does not take into account the estimation of the robots paths.

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 will play an essential role in agriculture. Deploying agricultural robots on the farm is still a challenging task due to the terrain's irregularity and size. Optimal path planning solutions may fail in larger terrains due to memory requirements as the search space increases. This work presents a novel open-source solution called AgRob Topologic Path Planner, which is capable of performing path planning operations using a hybrid map with topological and metric representations. A local A* algorithm pre-plans and saves local paths in local metric maps, saving them into the topological structure. Then, a graph-based A* performs a global search in the topological map, using the saved local paths to provide the full trajectory. Our results demonstrate that this solution could handle large maps (5 hectares) using just 0.002 % of the search space required by a previous solution.