Predictive maintenance on infrastructures is currently a hot topic. Its importance is proportional to the damages resulting from the collapse of the infrastructure. Bridges, dams and tunnels are placed on top on the scale of severity of potential damages due to the fact that they can cause loss of lives. Traditional inspection methods are not objective, tied to the inspector’s experience and require human presence on site. To overpass the limits of the current technologies and methods, the authors of this paper developed a unique new concept: a remote visual inspection system to perform predictive maintenance on infrastructures such as bridges. This is based on the fusion between advanced robotic technologies and the Automated Visual Inspection that guarantees objective results, high-level of safety and low processing time of the results.

This paper introduces a novel methodology developed for creating 3D models of archaeological artifacts that reduces the time and effort required by operators. The approach uses a simple vision system mounted on a robotic arm that follows a predetermined path around the object to be reconstructed. The robotic system captures different viewing angles of the object and assigns 3D coordinates corresponding to the robot's pose, allowing it to adjust the trajectory to accommodate objects of various shapes and sizes. The angular displacement between consecutive acquisitions can also be fine-tuned based on the desired final resolution. This flexible approach is suitable for different object sizes, textures, and levels of detail, making it ideal for both large volumes with low detail and small volumes with high detail. The recorded images and assigned coordinates are fed into a constrained implementation of the structure-from-motion (SfM) algorithm, which uses the scale-invariant features transform (SIFT) method to detect key points in each image. By utilising a priori knowledge of the coordinates and SIFT algorithm, low processing time can be ensured while maintaining high accuracy in the final reconstruction.The use of a robotic system to acquire images at a pre-defined pace ensures high repeatability and consistency across different 3D reconstructions, eliminating operator errors in the workflow. This approach not only allows for comparisons between similar objects but also provides the ability to track structural changes of the same object over time.Overall, the proposed methodology provides a significant improvement over current photogrammetry techniques by reducing the time and effort required to create 3D models while maintaining a high level of accuracy and repeatability.

Fused Deposition Modeling (FDM) is a type of Additive Manufacturing (AM) technology that is becoming increasingly common in numerous applications thanks to its versatility and reduced material waste. However, the complex physical phenomena occurring during extrusion, including the dynamics of non-Newtonian fluids, viscoelastic behaviors and rheology, make the use of heuristic observations preferable to that of analytical approaches. Consequently, engineers have focused on optimizing materials and hardware rather than control algorithms. The limited knowledge about extrusion and deposition dynamics usually confines the control action to the motion of the printing head while keeping a constant flow rate. Existing attempts to synchronize motion and extrusion consists of open loop compensations, which, however, require identified transfer functions or need to be tuned manually. This article aims to compactly review FDM technologies from a control perspective by presenting (i) the models of extrusion and deposition and (ii) the control strategies currently adopted in industry.

Sewing flexible materials such as textiles and clothing can be challenging due to their tendency to wrinkle easily and their non-linear mechanical behaviour. Conventional methods in industrial plants are performed by workers and can be labour-intensive and time-consuming. Therefore, the interest in robotic solutions has grown in the last decade. In this paper, we propose a flexible and reliable robotic solution that can autonomously remove wrinkles from fabric. This method was designed as a part of a robotic cell capable of sewing together two different textiles, used in the manufacturing of cyclist garments. The robotic system employs two compliant soft fingers to stretch the fabric and a vision system to identify the wrinkles to flatten. The design of the fingers is bio-inspired, mimicking the adaptability and dexterity of biological systems, hence improving the gripping performance while reducing the risk of damage to the fabric. The developed vision system performs instance segmentation to identify the wrinkles on the fabric, and then identifies the best places to apply the gripper to flatten the tissue. This two-step process is iterated until wrinkles on the surface do not affect the final sewn product. Such a methodology is highly flexible and has no hard requirements, as the vision system requires only an RGB camera, and the fingers are 3D-printed, an affordable and common manufacturing process. Consequently, the system proposed in this paper can be easily employed in a wide variety of industrial scenarios, improving the productivity and the welfare of the workers.