Abstract
Logistics chains are being increasingly developed due to several factors, among which the exponential growth of e-commerce. Cross-docking is a logistics strategy used by several companies from varied economic sectors, applied in warehouses and distribution centres. In this context, it is the objective of the CrossLog - Automatic Mixed-Palletizing for Crossdocking Logistics Centers Project, to investigate and study an automated and collaborative crossdocking system, capable of moving and managing the flow of products within the warehouse in the fastest and safest way. In its scope, this paper describes the modelling and simulation of a crossdocking system, with an integrated AS/RS, to analyse possible alternatives including not only the fast movement of products, but also its storage in case needed. Different scenarios were modelled and simulated, on the FlexSim software, and the obtained results for each one were critically analysed to draw conclusions on the best storage policy.

Abstract
Due to the lack of unskilled labour force that has been verified in the last years, several processes have been automated, both at industrial and services level. In terms of logistics tasks and transport of materials, it is increasingly common to use mobile robots, given the advantages that this equipment presents. This is also the case in airports, where the adoption of these vehicles to perform several tasks is becoming visible. Considering the possibility of using mobile robots to transport luggage at the Francisco Sa, Carneiro Airport, this paper presents the development of a simulation model and the analysis of several scenarios, with different number of vehicles, in order to understand the time that passengers would have to wait for their luggage, in case this task is automated. The final objective is to determine the number of vehicles required and the changes that need to be made to the airport's operation in order to ensure a level of service identical to (or better than) that currently achieved, with these operations being carried out by human operators.

Abstract

Abstract
The increasing world population, growing need for agricultural products, and labour shortages have driven the growth of robotics in agriculture. Tasks such as fruit harvesting require extensive hours of work during harvest periods and can be physically exhausting. Autonomous robots bring more efficiency to agricultural tasks with the possibility of working continuously. This paper proposes a stackable 3 DoF SCARA manipulator for tomato harvesting. The manipulator uses a custom electronic circuit to control DC motors with an endless gear at each joint and uses a camera and a Tensor Processing Unit (TPU) for fruit detection. Cascaded PID controllers are used to control the joints with magnetic encoders for rotational feedback, and a time-of-flight sensor for prismatic movement feedback. Tomatoes are detected using an algorithm that finds regions of interest with the red colour present and sends these regions of interest to an image classifier that evaluates whether or not a tomato is present. With this, the system calculates the position of the tomato using stereo vision obtained from a monocular camera combined with the prismatic movement of the manipulator. As a result, the manipulator was able to position itself very close to the target in less than 3 seconds, where an end-effector could adjust its position for the picking.

Abstract
To produce more food and tackle the labor scarcity, agriculture needs safer robots for repetitive and unsafe tasks (such as spraying). The interaction between humans and robots presents some challenges to ensure a certifiable safe collaboration between human-robot, a reliable system that does not damage goods and plants, in a context where the environment is mostly dynamic, due to the constant environment changes. A well-known solution to this problem is the implementation of real-time collision avoidance systems. This paper presents a global overview about state of the art methods implemented in the agricultural environment that ensure human-robot collaboration according to recognised industry standards. To complement are addressed the gaps and possible specifications that need to be clarified in future standards, taking into consideration the human-machine safety requirements for agricultural autonomous mobile robots.

Abstract
Bin picking is a challenging task involving many research domains within the perception and grasping fields, for which there are no perfect and reliable solutions available that are applicable to a wide range of unstructured and cluttered environments present in industrial factories and logistics centers. This paper contributes with research on the topic of object segmentation in cluttered scenarios, independent of previous object shape knowledge, for textured and textureless objects. In addition, it addresses the demand for extended datasets in deep learning tasks with realistic data. We propose a solution using a Mask R-CNN for 2D object segmentation, trained with real data acquired from a RGB-D sensor and synthetic data generated in Blender, combined with 3D point-cloud segmentation to extract a segmented point cloud belonging to a single object from the bin. Next, it is employed a re-configurable pipeline for 6-DoF object pose estimation, followed by a grasp planner to select a feasible grasp pose. The experimental results show that the object segmentation approach is efficient and accurate in cluttered scenarios with several occlusions. The neural network model was trained with both real and simulated data, enhancing the success rate from the previous classical segmentation, displaying an overall grasping success rate of 87.5%.