Abstract
Nowadays, entering in the highly competitive international market becomes a key strategy for the survive and sustained growth of enterprises in the Portuguese textile and footwear industrial sector. Thereby, to face new requirements, companies need to understand that technological innovation is a key issue. In this scenario, the research presented in this paper focuses on the development of a robot based conveyor line pick-and-place solution to perform an automatic collection of the shoe last. The solution developed consists of extracting the 3D model of the shoe last suport transported in the conveyor line and aligning it, using the Iterative Closest Point (ICP) algorithm, with a template model previously recorded. The Camera-Laser triangulation system was the approach selected to extract the 3D model. With the correct position and orientation estimation of the conveyor footwear, it will make possible to execute the pick-and-place task using an industrial manipulator. The practical implication of this work is that it contributes to improve the footwear production lines efficiency, in order to meet demands in shorter periods of time, and with high quality standards. This work was developed in partnership with the Portuguese company CEI by ZIPOR.

Abstract
This paper demonstrates the potential benefits of the integration of robot based sensing and Enterprise Information Systems extended with information about the geometric location and volumetric information of the parts contained in logistic supermarkets. The comparison of this extended world model with hierarchical spatial representations produced by a fleet of robots traversing the logistic supermarket corridors enables the continuous assessment of inconsistencies between reality, i.e., the spatial representations collected from online 3D data, and the modelled information, i.e., the world model. Results show that it is possible to detect inconsistencies reliably and in real time. The proposed approach contributes to the development of more robust and effective Enterprise Information Systems.

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.

Abstract
Industrial laser cutting machines use a type of support base that sometimes causes the cut metal parts to tilt or fall, which hinders the robot from picking the parts after cutting. The objective of this work is to calculate the 3D orientation of these metal parts with relation to the main metal sheet to successfully perform the subsequent robotic pick-and-place operation. For the perception part the system relies on the low cost 3D sensing Microsoft Kinect, which is responsible for mapping the environment. The previously known part positions are mapped in the new environment and then a plane fitting algorithm is applied to obtain its 3D orientation. The implemented algorithm is able to detect if the piece has fallen or not. If not, the algorithm calculates the orientation of each piece separately. This information is later used for the robot manipulator to perform the pick-and-place operation with the correct tool orientation. This makes it possible to automate a manufacturing process that is entirely human dependent nowadays.

Abstract
The objective of this work is to develop a highly robust 3D part localization and recognition algorithm. This research work is driven by the needs specified by enterprises with small production series that seek for full robotic automation in their production line, which processes a wide range of products and cannot use dedicated identification devices due to technological processes. With the correct classification of the part, the robot will be able to autonomously select the correct program to execute. For this purpose, the Perfect Match algorithm, which is known by its computational efficiency, high precision and robustness, was adapted for object recognition achieving a 99.7% of classification rate. The expected practical implication of this work is contributing to the integration of industrial robots in highly dynamic and specialized lines, reducing the companies' dependency on skilled operators.

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.