Abstract
Supply chains are ubiquitous in any commercial delivery systems. The exchange of goods and services, from different supply points to distinct destinations scattered along a given geographical area, requires the management of stocks and vehicles fleets in order to minimize costs while maintaining good quality services. Even if the operating conditions remain constant over a given time horizon, managing a supply chain is a very complex task. Its complexity increases exponentially with both the number of network nodes and the dynamical operational changes. Moreover, the management system must be adaptive in order to easily cope with several disturbances such as machinery and vehicles breakdowns or changes in demand. This work proposes the use of a model predictive control paradigm in order to tackle the above referred issues. The obtained simulation results suggest that this strategy promotes an easy tasks rescheduling in case of disturbances or anticipated changes in operating conditions. © Springer International Publishing Switzerland 2017.

Abstract
Control applications are a key aspect of current industrial environments. Regarding cork industries, there is a particular process that needs to be addressed: the cork stoppers drying. Currently the methodology used in this process delays the overall production cycle and lacks in the drying efficiency itself. This paper presents the development of a cork stopper drying system based on the control of a conveyor based machine using Model Predictive Control (MPC). Throughout the project itwas also developed a drying kineticsmodel for the cork stoppers and an extension of such model to a discrete space state model. By applying the proposed methodology it is assured the cork stoppers’ drying in a faster and more efficient way. © Springer International Publishing Switzerland 2017.

Abstract
Renewable sources of energy play a decisive role in the current energetic paradigm to mitigate climate changes associated with greenhouse gases emissions and problems of energy security. Biomass energy and in particular forest wood biomass supply chains have the potential to enhance these changes due to its several benefits such as ability to produce both bioenergy and bioproducts, generate energy on-demand, among others. However, this energy source has some drawbacks mainly associated with the involved costs. In this work, the use of a Model Predictive Control approach is proposed to plan, monitor and control the wood-biomass supply chain for energy production at a tactical level. With this methodology the biomass supply chain becomes more efficient ensuring the service quality in a more competitive way. In order to test and validate the proposed approach different simulation scenarios were considered that proved the efficiency of the proposed tool regarding the decisions definition and control.

Abstract
In this paper we address the allocation of perception tasks among a set of multiple robots, for tasks such as inspection, surveillance, or search in structured environments. We consider a set of target regions of interest in a mapped environment that need to be sensed by any of the robots, and the problem is to find paths for the robots that cover all the target regions with minimal cost. We consider not only sensing range when determining paths for the robots to perceive the targets, but also a sensor cost function that can be adapted to each robot’s sensor. Thus the planning has to search for paths with minimal motion and perception cost, instead of the traditional approach where line-of-sight is the only requirement in a motion cost minimization problem. Our contribution is to use planning to determine possible perception positions for every robot, which we cluster and then use as possible waypoints that can be used to construct paths for all the robots. Given the combinatorial characteristics of path determination in this setting, we contribute a construction heuristic to find paths that guarantee full coverage of all the feasible perception target regions, while minimizing the overall cost. We assume robots are heterogeneous regarding their geometric properties, such as size and maximum perception range. We consider simulated scenarios where we show the benefits of our approach, enabling multi-robot path planning for perception of multiple regions of interest. © Springer International Publishing AG 2018.

Abstract
Over the last decades, Serious Games have gained increased importance, mainly due to the evolution and expansion of video games and its application in multiple areas. Integration in the world of sport is one of the solutions that individuals with disabilities or motor disorders develop to feel more socially integrated, more independent and confident with themselves. Boccia is a Paraly-mpic sport that is increasingly getting more attention around the world. This has contributed for the objectives of this project since it attracts these patients a lot more easily and including it in the Serious Games category enables them to develop and rehabilitate their cognitive capabilities. It will allow the users being dynamic, holding their attention and motivating them instead of the traditional cognitive rehabilitation processes that quickly become repetitive and discouraging. This paper describes a realistic Boccia game simulator adapted for people with disabilities or motor disorders still on development that aims to integrate a set of features that include real physics, multimodal user interface and social features (diversion, rehabilitation, competition and improvement). These features can be used to enhance the interest of non-practitioners of the sport and to improve the training conditions of Boccia athletes. Results observed in an experiment with real Boccia game components indicate that the simulator offers great similarity to the reality with the maximum difference between the measures obtained in both being 10 cm.

Abstract
This paper aims to develop grasping and manipulation capability along with autonomous navigation and localization in a wheelchair-mounted robotic arm to serve patients. Since the human daily environment is dynamically varied, it is not possible to enable the robot to know all the objects that would be grasped. We present an approach to enable the robot to detect, grasp and manipulate unknown objects. We propose an approach to construct the local reference frame that can estimate the object pose for detecting the grasp pose of an object. The main objective of this paper is to present the grasping and manipulation approach along with a navigating and localization method that can be performed in the human daily environment. A grid map and a match algorithm is used to enable the wheelchair to localize itself using a low-power computer. The experimental results show that the robot can manipulate multiple objects and can localize itself with great accuracy.