Abstract
Localization and mapping of autonomous robots in a hard and unstable environment (Steep Slope Vineyards) is a challenging research topic. Typically, the commonly used dead reckoning systems can fail due to the harsh conditions of the terrain and the Global Position System (GPS) accuracy can be considerably noisy or not always available. One solution is to use wireless sensors in a network as landmarks. This paper evaluates a ultra-wideband time-of-flight based technology (Pozyx), which can be used as cost-effective solution for application in agricultural robots that works in harsh environment. Moreover, this paper implements a Localization Extended Kalman Filter (EKF) that fuses odometry with the Pozyx Range measurements to increase the default Pozyx Algorithm accuracy. © Springer International Publishing AG 2018.

Abstract
In this paper it is presented a low cost experiment based on a small Arduino based prototype. The chosen educational challenge is a classical introductory experiment, that consists in following a line with a mobile robot. The presented experiment has as goal to introduce students to mobile robotics, having as base a challenge and a kinematics that are commonly applied in Junior competitions. A group of students participated in a workshop that consisted, initially, in a lecture where tutors explained the differential robot kinematics and how to develop a controller for the proposed challenge. Then the students, after the theoretical introduction, implemented the proposed robot controller.

Abstract
Object tracking in a moving frame is becoming a common requirement in a lot of mobile robotic applications, such as search and rescue, monitoring and surveillance, and even in some scientific applications, such as robotic soccer. In all these applications, the robots must be capable of estimating the target position and, sometimes, velocity on their own. Depending on the application and on the current scene situation, the estimates must be more or less accurate, depending on the robot intention to interact with the target, whether to catch it, follow it, etc. The problem is that a robot moves along the working area, having some uncertainty in its pose estimation. This paper proposes an approach based on a Kalman Filter to estimate the object position and velocity, regardless the robot pose. As a testbed, a Middle-Size League soccer robot tracking a moving ball example will be used. This approach allows the agent to follow and interact with a moving object, even if its localization is not available. © Springer International Publishing AG 2018.

Abstract
Cable-driven robots have received some attention by the scientific community and, recently, by the industry because they can transport hazardous materials with a high level of safeness which is often required by construction sites. In this context, this research presents an extension of a cable-driven robot called SPIDERobot, that was developed for automated construction of architectural projects. The proposed robot is formed by a rotating claw and a set of four cables, enabling four degrees of freedom. In addition, this paper proposes a new Vision-Guided Path-Planning System (V-GPP) that provides a visual interpretation of the scene: the position of the robot, the target and obstacles location; and optimizes the trajectory of the robot. Moreover, it determines a collision-free trajectory in 3D that takes into account the obstacles and the interaction of the cables with the scene. A set of experiments make possible to validate the contribution of V-GPP to the SPIDERobot while operating in realistic working conditions, as well as, to evaluate the interaction between the V-GPP and the motion controlling system. The results demonstrated that the proposed robot is able to construct architectural structures and to avoid collisions with obstacles in their working environment. The V-GPP system localizes the robot with a precision of 0.006 m, detects the targets and successfully generates a path that takes into account the displacement of cables. Therefore, the results demonstrate that the SPIDERobot can be scaled up to real working conditions.

Abstract
Object localization and tracking is core to many practical applications, like human-computer interaction, security and surveillance, robot competitions and Industry 4.0. Such task may be computationally demanding, especially for traditional embedded systems, that usually have tight processing and storage constraints. This calls for the investigation of alternatives, including emergent heterogeneous embedded systems, like the Parallella line of single-board-computers (SBCs). The work presented in this paper explores the use of a Parallella board with a 16-core Epiphany co-processor, to perform real-time tracking of objects in frames captured by a Kinect sensor, based on color segmentation. We addressed several processing strategies, trying to assess which one performed better. We also ran the same code (where applicable) in several models of the Raspberry Pi platform, for comparison. We conclude that effectively exploring the Epiphany co-processor is not trivial, requiring considerable programming effort and suitable applications (CPU-demanding and highly parallelizable), to the extent that simpler development approaches, on more recent SBCs may be more effective. © 2017.

Abstract
Forest biomass has gained increasing interest in the recent years as a renewable source of energy in the context of climate changes and continuous rising of fossil fuels prices. However, due to its characteristics such as seasonality, low density, and high cost, the biomass supply chain needs further optimization to become more competitive in the current energetic market. In this sense and taking into consideration the fact that the transportation is the process that accounts for the higher parcel in the biomass supply chain costs, this work proposes a multilayer model predictive control based strategy to improve the performance of this process at the operational level. The proposed strategy aims to improve the overall supply chain performance by forecasting the system evolution using behavioural dynamic models. In this way, it is possible to react beforehand and avoid expensive impacts in the tasks execution. The methodology is composed of two interconnected levels that closely monitor the system state update, in the operational level, and delineate a new routing and scheduling plan in case of an expected deviation from the original one. By applying this approach to an experimental case study, the concept of the proposed methodology was proven. This novel strategy enables the online scheduling of the supply chain transport operation using a predictive approach.