Abstract
The sensors that acquire 3D data play an important role in many applications. In addition, they have been used in the robotic field for several purposes, for instance, enhancing the navigation of mobile robots, object detection, scene reconstruction, 3D inspection of parts and others. Moreover, a significant amount of devices with distinct cost, accuracy and features have been released in the recent years which increases the difficulty of comparing each sensor in a proper manner or choosing the most suitable device for a specific task and operation field. This paper compares the Kinect v1, Kinect v2, Structure Sensor and Mesa Imaging SR4000. The noise of each sensor is characterized for different distances and considering objects with different colors. Therefore, this paper proposes a simple but quantitative benchmark for evaluating 3D devices that characterizes the most relevant features for the robotic field and in accordance with different type of operations.

Abstract

Abstract
Aim of study: This work aims to provide an overview of Model Predictive Controllers (MPC) applications in supply chains, to describe the forest-based supply chain and to analyse the potential use and benefits of MPC in a case study concerning a biomass supply chain. Area of study: The proposed methods are being applied to a company located in Finland. Material and methods: Supply chains are complex systems where actions and partners' coordination influence the whole system performance. The increase of competitiveness and need of quick responses to the costumers implies the use of efficient management techniques. The control theory, particularly MPC, has been successfully used as a supply chain management tool. MPC is able to deal with dynamic interactions between the partners and to globally optimize the supply chain performance in the presence of disturbances. However, as far as is authors' knowledge, there are no applications of this methodology in the forest-based supply chains. This work proposes a control architecture to improve the performance of the forest supply chain. The controller is based on prediction models which are able to simulate the system and deal with disturbances. Main results: The preliminary results enable to evaluate the impacts of disturbances in the supply chain. Thus, it is possible to react beforehand, controlling the schedules and tasks' allocation, or alert the planning level in order to generate a new plan. Research highlights: Overview of MPC applications in supply chains; forest-based supply chain description; case study presentation: wood biomass supply chain for energy production; MPC architecture proposal to decrease the operation times.

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In this paper the authors focus on presenting a new path planning approach for a multi-robot transportation system in an industrial case scenario. The proposed method is based on the A* heuristic search in a cell decomposition scenario, for which a time component was added - Time Enhanced A* or simply TEA*. To access the flexibility and efficiency of the proposed algorithm, a set of experiments were performed in a simulated industrial environment. During trials execution the proposed algorithm has shown high capability on preventing/dealing with the occurrence of deadlocks in the transportation system.

Abstract
This paper presents an algorithm capable of generating smooth, feasible paths for an any-shape non-holonomic mobile robot, taking into account orientation restrictions, with the aim of navigating close to obstacles. Our contribution consists in an extension of the A* algorithm in a cell decomposition, where besides its position, the orientation of the platform is also considered when searching for a path. This is achieved by constructing 16 layers of orientations and only visiting neighbor layers when searching for the lowest cost. To simplify collision checking, the robot's footprint is used to inflate obstacles, yet, to allow the robot to find paths close to obstacles, the actual footprint of the robot must used. By discretizing the orientation space into layers and computing an oriented footprint for each layer, the actual footprint of the robot is used, increasing the configuration space without becoming computationally expensive. The path planning algorithm was developed under the EU-funded project CARLoS(1) and was implemented in a stud welding robot simulated within a naval industry environment, validating our approach.

Abstract
Traffic Control is one of the fundamental problems in the management of an Automated Guided Vehicle (AGV) system. Its main objectives are to assure efficient conflict free routes and to avoid/solve system deadlocks. In this sense, and as an extension of our previouswork, this paper focus on exploring the capabilities of the Time Enhanced A* (TEA*) to dynamically control a fleet of AGVs, responsible for the execution of a predetermined set of tasks, considering an automatic warehouse case scenario. During the trial execution the proposed algorithm, besides having shown high capability on preventing/dealing with the occurrence of deadlocks, it also has exhibited high efficiency in the generation of free collision trajectories. Moreover, it was also selected an alternative from the state-of-art, in order to validate the TEA* results and compare it.