Lazaros Nalpantidis

The safety of robotics and automation technologies is a significant concern for stakeholders in Industry 5.0. Ensuring cost-effectiveness and inherent safety requires applying the defense-in-depth principle. This paper introduces a novel risk-informed design framework for functional safety, integrating function-centered hazard identification and risk assessment via fault tree analysis (FTA). Demonstrated in the design of a semi-automated agricultural vehicle, the framework begins with a function-centered hazard identification approach (F-CHIA) based on ISO 12100. It examined design intents, identified hazard zones, and conducted task and function identification. Foreseeable functional hazardous situations are analyzed, leading to functional requirements and the identification of relevant directives, regulations, and standards. The F-CHIA outputs inform the functional safety analysis, assessing the required performance level and deriving specific requirements for software, hardware, and human operators using FTA. The functional requirements derived from F-CHIA are more systematic than traditional methods and serve as effective inputs for functional safety analysis in human–robot collaboration applications. The proposed framework enables design teams to focus on enhancing factors that improve functional safety performance levels, resulting in a more thorough and effective safety design process.

The appearance of construction defects in buildings can arise from a variety of factors, ranging from issues during the design and construction phases to problems that develop over time with the lifecycle of a building. These defects require repairs, often in the context of a significant shortage of skilled labor. In addition, such work is often physically demanding and carried out in hazardous environments. Consequently, adopting autonomous robotic systems in the construction industry becomes essential, as they can relieve labor shortages, promote safety, and enhance the quality and efficiency of repair and maintenance tasks. Hereupon, the present study introduces an end-to-end framework towards the automation of shotcreting tasks in cases where construction or repair actions are required. The proposed system can scan a construction scene using a stereo-vision camera mounted on a robotic platform, identify regions of defects, and reconstruct a 3D model of these areas. Furthermore, it automatically calculates the required 3D volumes to be constructed to treat a detected defect. To achieve all of the above-mentioned technological tools, the developed software framework employs semantic segmentation and 3D reconstruction modules based on YOLOv8m-seg, SiamMask, InfiniTAM, and RTAB-Map, respectively. In addition, the segmented 3D regions are processed by the volumetric modeling component, which determines the amount of concrete needed to fill the defects. It generates the exact 3D model that can repair the investigated defect. Finally, the precision and effectiveness of the proposed pipeline are evaluated in actual construction site scenarios, featuring reinforcement bars as defective areas.

The importance of construction automation has grown worldwide, aiming to deliver new machineries for the automation of roads, tunnels, bridges, buildings and earth-work construction. This need is mainly driven by (i) the shortage and rising costs of skilled workers, (ii) the tremendous increased needs for new infrastructures to serve the daily activities and (iii) the immense demand for maintenance of ageing infrastructure. Shotcrete (sprayed concrete) is increasingly becoming popular technology among contractors and builders, as its application is extremely economical and flexible as the growth in construction repairs in developed countries demand excessive automation of concrete placement. Even if shotcrete technology is heavily mechanized, the actual application is still performed manually at a large extend. RoBétArméEuropean project targets the Construction 4.0 transformation of the construction with shotcrete with the adoption of breakthrough technologies such as sensors, augmented reality systems, high-performance computing, additive manufacturing, advanced materials, autonomous robots and simulation systems, technologies that have already been studied and applied so far in Industry 4.0. The paper at hand showcases the development of a novel robotic system with advanced perception, cognition and digitization capabilities for the automation of all phases of shotcrete application. In particular, the challenges and barriers in shotcrete automation are presented and the RoBétArmésuggested solutions are outlined. We introduce a basic conceptual architecture of the system to be developed and we demonstrate the four application scenarios on which the system is designated to operate.

Robot perception is far from what humans are capable of. Humans do not only have a complex semantic scene understanding but also extract fine-grained intra-object properties for the salient ones. When humans look at plants, they naturally perceive the plant architecture with its individual leaves and branching system. In this work, we want to advance the granularity in plant understanding for agricultural precision robots. We develop a model to extract fine-grained phenotypic information, such as leaf-, stem-, and vein instances. The underlying dataset RumexLeaves is made publicly available and is the first of its kind with keypoint-guided polyline annotations leading along the line from the lowest stem point along the leaf basal to the leaf apex. Furthermore, we introduce an adapted metric POKS complying with the concept of keypoint-guided polylines. In our experimental evaluation, we provide baseline results for our newly introduced dataset while showcasing the benefits of POKS over OKS.