Abstract
Logistics chains are being increasingly developed due to several factors, among which the exponential growth of e-commerce. Crossdocking 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 concept and architecture envisioned for the crossdocking system developed in the scope of the CrossLog Project. One of its main distinguishing characteristics is the use of Autonomous Mobile Robots for performing much of the operations traditionally performed by human operators in today's logistics centres.

Abstract
Quality control inspection systems are crucial and a key factor in maintaining and ensuring the integrity of any product. The quality inspection task is a repetitive task, when performed by operators only, it can be slow and susceptible to failures due to the lack of attention and fatigue. This work focuses on the inspection of parts made of high-pressure diecast aluminum for components of the automotive industry. In the present case study, last year, 18240 parts needed to be reinspected, requiring approximately 96 hours, a time that could be spent on other tasks. This article performs a comparison of four deep learning models: Faster R-CNN, RetinaNet, YOLOv7, and YOLOv7-tiny, to find out which one is more suited to perform the quality inspection task of detecting metal filings on casting aluminum parts. As for this use-case the prototype must be highly intolerant to False Negatives, that is, the part being defective and passing undetected, Faster R-CNN was considered the bestperforming model based on a Recall value of 96.00%.

Abstract

Abstract
Different strategies have been adopted for the optimization of legged robots, either during their design and construction phases, or during their operation. Evolutionary strategies are a way to imitate nature replicating the process that nature designed for the generation and evolution of species. This paper presents a genetic algorithm, running over a simulation application of legged robots, that allows the optimization of several locomotion, model and controller parameters, for different locomotion speeds and gaits. Here are studied the model and locomotion parameters that optimize the robot performance, in a large range of distinct velocities.

Abstract
This paper deals with the discretization of integrals and derivatives (i.e., differintegrals) of complex order. Several methods for the discretization of the operator s?, where ? = u+jv is a complex value, are proposed. The concept of conjugated-order differintegral is also presented. The conjugated-order operator allows the use of complex-order differintegrals while still resulting in real time responses and real transfer functions. The performance of the resulting approximations is evaluated both in the time and frequency domains. Copyright © 2006 IFAC.

Abstract
This paper presents the mechanical construction of a climbing robot with wheeled locomotion and adhesion through permanent magnets. This machine is intended to be used in the inspection of different types of ferromagnetic structures, in order to, for instance, detect weaknesses due to corrosion, particularly in fuel tanks, ship hulls, etc. The vehicle will have a semi-autonomous behaviour, allowing a remote inspection process controlled by a technician, this way reducing the risks associated with the inspection of tall structures and ATEX places. The distinguishing characteristic of this robot is its dynamic adjustment system of the permanent magnets in order to assure the machine adhesion to the surfaces, even when crossing irregular and curved surfaces.