Abstract
Worldwide, forests have been devastated by fires in recent years. Whe- ther by human intervention or for other reasons, the history of burned areas is increasing year after year, degrading fauna and flora. For this reason, it is vital to detect an early ignition so that firefighters can act quickly, reducing the impacts caused by forest fires. The proposed system aims to improve the nature monitoring and to assist the existing surveillance systems through Wireless Sensor Network. The network formed by the set of sensors has the potential to identify forest ignitions and, consequently, alerts the authorities through LoRaWAN communication. This work presents a prototype based on low-cost technology, which can be used in areas that require a high density of modules. Tests with a Wireless Sensor Network made up of nine prototypes demonstrate its effectiveness and robustness in terms of data transmission and collection. In this way, it is possible to apply this approach in Portuguese forests with a high level of forest fire risk, transforming them into Forests 4.0 concept.

Abstract
Inspection based on mobile autonomous robots can assume an important role in many industries. Instead of having fixed sensors, the concept of assembling the sensors on a mobile robot that performs the scanning and inspection through a defined path is cheaper, configurable and adaptable. This paper describes a mobile robot, equipped with several gas sensors and a LIDAR device, that scans an established area by following a trajectory based on way-points searching for gas leakage and simultaneously avoid obstacles in the map. In other words, the robot follows the trajectory while the gas concentration is under a defined value and surrounding the obstacles. Otherwise, the autonomous robot starts the leakage search based on a search algorithm that allows to find the leakage position. The proposed methodology is verified in simulation based on a model of the real robot. The search test performed in a simulation environment allows to validate the proposed methodology.

Abstract
Automobile industry faces one of the most flexible productivity caused by the number of customized models variants due to the buyers needs. This fact requires the production system to introduce flexible, adaptable and cooperative with humans solutions. In the present work, a panel that should be mounted inside a van is addressed. For that purpose, a mobile manipulator is suggested that could share the same space with workers helping each other. This paper presents the navigation system for the robot that enters the van from the rear door after a ramp, operates and exits. The localization system is based on 3DOF methodologies that allow the robot to operate autonomously. Real tests scenarios prove the precision and repeatability of the navigation system outside, inside and during the ramp access of the van.

Abstract
Frequently carrying high loads and performing repetitive tasks compromises the ergonomics of individuals, a recurrent scenario in hospital environments. In this paper, we design a logistic planner of a fleet of autonomous mobile robots for the automation of transporting trolleys around the hospital, which is independent of the space configuration, and robust to loss of network and deadlocks. Our robotic solution has an innovative gripping system capable of grasping and pulling non-modified standard trolleys just by coupling a plate. Robots are able to navigate autonomously, to avoid obstacles assuring the safety of operators, to identify and dock a trolley, to access charging stations and elevators, and to communicate with the latter. An interface was built allowing users to command the robots through a web server. It is shown how the proposed methodology behaves in experiments conducted at the Faculty of Engineering of the University of Porto and Braga's Hospital.

Abstract
Steep slope vineyards are a complex scenario for the development of ground robots. Planning a safe robot trajectory is one of the biggest challenges in this scenario, characterized by irregular surfaces and strong slopes (more than 35 degrees). Moving the robot through a pile of stones, spots with high slope or/and with wrong robot yaw may result in an abrupt fall of the robot, damaging the equipment and centenary vines, and sometimes imposing injuries to humans. This paper presents a novel approach for path planning aware of center of mass of the robot for application in sloppy terrains. Agricultural robotic path planning (AgRobPP) is a framework that considers the A* algorithm by expanding inner functions to deal with three main inputs: multi-layer occupation grid map, altitude map and robot's center of mass. This multi-layer grid map is updated by obstacles taking into account the terrain slope and maximum robot posture. AgRobPP is also extended with algorithms for local trajectory replanning during the execution of a trajectory that is blocked by the presence of an obstacle, always assuring the safety of the re-planned path. AgRobPP has a novel PointCloud translator algorithm called PointCloud to grid map and digital elevation model (PC2GD), which extracts the occupation grid map and digital elevation model from a PointCloud. This can be used in AgRobPP core algorithms and farm management intelligent systems as well. AgRobPP algorithms demonstrate a great performance with the real data acquired from AgRob V16, a robotic platform developed for autonomous navigation in steep slope vineyards.

Abstract
Forests have been harassed by fire in recent years. Whether by human action or for other reasons, the burned area has increased harming fauna and flora. It is fundamental to detect an ignition early in order to firefighters fight the fire minimizing the fire impacts. The proposed Forest Monitoring System aims to improve the nature monitoring and to enhance the existing surveillance systems. A set of innovative operations is proposed that will allow to identify a forest ignition and also will monitor the fauna. For that, a set of sensors are being developed and placed in the forest to transmit data and identify forest fire ignition. This paper addresses a methodology that identifies the optimal positions to place the developed sensors in order to minimize the fire hazard. Some preliminary results are shown by a stochastic algorithm that spread points to position the sensor modules in areas with a high risk of fire hazard.