A 2D laser rangefinder scans dataset of standard EUR pallets

open5siopenIhab Mohamed, Alessio Capitanelli, Fulvio Mastrogiovanni, Stefano Rovetta, Renato ZaccariaMohamed, Ihab; Capitanelli, Alessio; Mastrogiovanni, Fulvio; Rovetta, Stefano; Zaccaria, RENATO UGO RAFFAEL

Detecting and tracking using 2D laser range finders and deep learning

Detecting and tracking people using 2D laser rangefinders (LRFs) is challenging due to the features of the human leg motion, high levels of self-occlusion and the existence of objects which are similar to the human legs. Previous approaches use datasets that are manually labelled with support of images of the scenes. We propose a system with a calibrated monocular camera and 2D LRF mounted on a mobile robot in order to generate a dataset of leg patterns through automatic labelling which is valid to achieve a robust and efficient 2D LRF-based people detector and tracker. First, both images and 2D laser data are recorded during the robot navigation in indoor environments. Second, the people detection boxes and keypoints obtained by a deep learning-based object detector are used to locate both people and their legs on the images. The coordinates frame of 2D laser is extrinsically calibrated to the camera coordinates allowing our system to automatically label the leg instances. The automatically labelled dataset is then used to achieve a leg detector by machine learning techniques. To validate the proposal, the leg detector is used to develop a Kalman filter-based people detection and tracking algorithm which is experimentally assessed. The experimentation shows that the proposed system overcomes the Angus Leigh’s detector and tracker which is considered the state of the art on 2D LRF-based people detector and tracker.This work was supported under Grant PID2019-104818RB-I00 funded by MCIN/AEI/10.13039/501100011033 and by ‘‘European Regional Development Fund (ERDF) A way of making Europe’’.Funding for open access charge: Universidad de Granada / CBUA

Automated Guided Vehicle utilising thermal signatures for Human identification and tracking

Published ThesisIndustry requires the development of sophisticated autonomous guided vehicles (AGV) with sensory and software capabilities to allow a vision-based awareness of surrounding objects. To achieve this, a closely integrated control system for the AGV together with machine vision capabilities needs to be developed to efficiently and reliably detect objects of interest. Industry application of AGVs require detection of humans and to support that requirement thermal imaging cameras offer a broad set of advantages. The aim of the study is to develop an AGV that uses a thermal imaging camera to detect a human in its environment. To achieve this, a literature study was done to determine the best type of components that should be used, reveal design issues and what characteristics the system must adhere to. LabVIEW was used to simulate AGV movement and operation together with the control system, develop machine vision capable of background noise filtering and verify the machine vision identification and tracking processes. Based on simulated results, the physical system was built and small modificationsmade to accommodate real world variables. The results indicate that a vision-based approach to detect, track and identify a person on a mobile robot in real time is achievable. It was found that LabVIEW is an excellent tool and platform for building the integrated system and expedites design and implementation. A key implication of this study is to show the versatility of thermal imaging as a method to extract a person from its background independently from current light conditions and in situations where full-colour cameras will fail

Industrial Segment Anything -- a Case Study in Aircraft Manufacturing, Intralogistics, Maintenance, Repair, and Overhaul

Deploying deep learning-based applications in specialized domains like the aircraft production industry typically suffers from the training data availability problem. Only a few datasets represent non-everyday objects, situations, and tasks. Recent advantages in research around Vision Foundation Models (VFM) opened a new area of tasks and models with high generalization capabilities in non-semantic and semantic predictions. As recently demonstrated by the Segment Anything Project, exploiting VFM's zero-shot capabilities is a promising direction in tackling the boundaries spanned by data, context, and sensor variety. Although, investigating its application within specific domains is subject to ongoing research. This paper contributes here by surveying applications of the SAM in aircraft production-specific use cases. We include manufacturing, intralogistics, as well as maintenance, repair, and overhaul processes, also representing a variety of other neighboring industrial domains. Besides presenting the various use cases, we further discuss the injection of domain knowledge

Smart Pipe System for a Shipyard 4.0

As a result of the progressive implantation of the Industry 4.0 paradigm, many industries are experimenting a revolution that shipyards cannot ignore. Therefore, the application of the principles of Industry 4.0 to shipyards are leading to the creation of Shipyards 4.0. Due to this, Navantia, one of the 10 largest shipbuilders in the world, is updating its whole inner workings to keep up with the near-future challenges that a Shipyard 4.0 will have to face. Such challenges can be divided into three groups: the vertical integration of production systems, the horizontal integration of a new generation of value creation networks, and the re-engineering of the entire production chain, making changes that affect the entire life cycle of each piece of a ship. Pipes, which exist in a huge number and varied typology on a ship, are one of the key pieces, and its monitoring constitutes a prospective cyber-physical system. Their improved identification, traceability, and indoor location, from production and through their life, can enhance shipyard productivity and safety. In order to perform such tasks, this article first conducts a thorough analysis of the shipyard environment. From this analysis, the essential hardware and software technical requirements are determined. Next, the concept of smart pipe is presented and defined as an object able to transmit signals periodically that allows for providing enhanced services in a shipyard. In order to build a smart pipe system, different technologies are selected and evaluated, concluding that passive and active RFID are currently the most appropriate technologies to create it. Furthermore, some promising indoor positioning results obtained in a pipe workshop are presented, showing that multi-antenna algorithms and Kalman filtering can help to stabilize Received Signal Strength (RSS) and improve the overall accuracy of the system.Comment: 43 pages, 25 figures, accepted version of Sensors journal articl

Sensors and Systems for Indoor Positioning

This reprint is a reprint of the articles that appeared in Sensors' (MDPI) Special Issue on “Sensors and Systems for Indoor Positioning". The published original contributions focused on systems and technologies to enable indoor applications

Yield sensing technologies for perennial and annual horticultural crops: a review

Yield maps provide a detailed account of crop production and potential revenue of a farm. This level of details enables a range of possibilities from improving input management, conducting on-farm experimentation, or generating profitability map, thus creating value for farmers. While this technology is widely available for field crops such as maize, soybean and grain, few yield sensing systems exist for horticultural crops such as berries, field vegetable or orchards. Nevertheless, a wide range of techniques and technologies have been investigated as potential means of sensing crop yield for horticultural crops. This paper reviews yield monitoring approaches that can be divided into proximal, either direct or indirect, and remote measurement principles. It reviews remote sensing as a way to estimate and forecast yield prior to harvest. For each approach, basic principles are explained as well as examples of application in horticultural crops and success rate. The different approaches provide whether a deterministic (direct measurement of weight for instance) or an empirical (capacitance measurements correlated to weight for instance) result, which may impact transferability. The discussion also covers the level of precision required for different tasks and the trend and future perspectives. This review demonstrated the need for more commercial solutions to map yield of horticultural crops. It also showed that several approaches have demonstrated high success rate and that combining technologies may be the best way to provide enough accuracy and robustness for future commercial systems

SpinX: Time-resolved 3D Analysis of Spindle Dynamics using Deep Learning Techniques and Mathematical Modelling.

PhD ThesesLive-cell movies generate terabytes of data. However, manual analysis of this data is prone to error and can easily exhaust days of research time, thus limiting the insights that can be gleaned from cutting edge microscopes. Automated analysis has been hard because of discontinuities between the distinct frames of 3D live-cell movies. We present SpinX, a comprehensive and extensible computational framework which bridges the gaps between discontinuous frames in time lapse movies by utilising state-of-the-art Deep Learning technologies and modelling for 3D reconstruction of highly mobile subcellular structures. Using SpinX, we are now in a position to precisely track and analyse the movements of multiple subcellular structures within minutes, including the cell cortex, chromosomes and the mitotic spindle. We demonstrate the utility of SpinX by employing it to define the precise roles of spindle movement regulators that ultimately determine the plane of cell division. We illustrate the extensibility of SpinX by showing how it can also be used to infer the regulation of complex cortex-microtubule interactions. Our analyses reveal previously unrecognised roles for the evolutionarily conserved Dynein motor and MARK2/Par1 polarity kinase in regulating the 3D movements of the mitotic spindle. Thus, SpinX provides an exciting opportunity to study spindle dynamics in relation to the cell cortex using hundreds of time-resolved 3D movies in a novel way

Simulation in Automated Guided Vehicle System Design

The intense global competition that manufacturing companies face today results in an increase of product variety and shorter product life cycles. One response to this threat is agile manufacturing concepts. This requires materials handling systems that are agile and capable of reconfiguration. As competition in the world marketplace becomes increasingly customer-driven, manufacturing environments must be highly reconfigurable and responsive to accommodate product and process changes, with rigid, static automation systems giving way to more flexible types. Automated Guided Vehicle Systems (AGVS) have such capabilities and AGV functionality has been developed to improve flexibility and diminish the traditional disadvantages of AGV-systems. The AGV-system design is however a multi-faceted problem with a large number of design factors of which many are correlating and interdependent. Available methods and techniques exhibit problems in supporting the whole design process. A research review of the work reported on AGVS development in combination with simulation revealed that of 39 papers only four were industrially related. Most work was on the conceptual design phase, but little has been reported on the detailed simulation of AGVS. Semi-autonomous vehicles (SA V) are an innovative concept to overcome the problems of inflexible -systems and to improve materials handling functionality. The SA V concept introduces a higher degree of autonomy in industrial AGV -systems with the man-in-the-Ioop. The introduction of autonomy in industrial applications is approached by explicitly controlling the level of autonomy at different occasions. The SA V s are easy to program and easily reconfigurable regarding navigation systems and material handling equipment. Novel approaches to materials handling like the SA V -concept place new requirements on the AGVS development and the use of simulation as a part of the process. Traditional AGV -system simulation approaches do not fully meet these requirements and the improved functionality of AGVs is not used to its full power. There is a considerflble potential in shortening the AGV -system design-cycle, and thus the manufacturing system design-cycle, and still achieve more accurate solutions well suited for MRS tasks. Recent developments in simulation tools for manufacturing have improved production engineering development and the tools are being adopted more widely in industry. For the development of AGV -systems this has not fully been exploited. Previous research has focused on the conceptual part of the design process and many simulation approaches to AGV -system design lack in validity. In this thesis a methodology is proposed for the structured development of AGV -systems using simulation. Elements of this methodology address the development of novel functionality. The objective of the first research case of this research study was to identify factors for industrial AGV -system simulation. The second research case focuses on simulation in the design of Semi-autonomous vehicles, and the third case evaluates a simulation based design framework. This research study has advanced development by offering a framework for developing testing and evaluating AGV -systems, based on concurrent development using a virtual environment. The ability to exploit unique or novel features of AGVs based on a virtual environment improves the potential of AGV-systems considerably.University of Skovde. European Commission for funding the INCO/COPERNICUS Projec