Fabio Oleari

Technological roadmap to boost the introduction of AGVs in industrial applications

This paper describes systems of multiple Automated Guided Vehicles (AGVs) used in factory logistics for the transportation of goods. We describe currently applied solutions, highlighting the main issues that, so far, have prevented a pervasive diffusion of these systems. A roadmap of technological solutions is then drafted, to improve the performance of AGV systems and boost their wide application in factory logistics.

Investigation of Vision-Based Underwater Object Detection with Multiple Datasets:

In this paper, we investigate the potential of vision-based object detection algorithms in underwater environments using several datasets to highlight the issues arising in different scenarios. Underwater computer vision has to cope with distortion and attenuation due to light propagation in water, and with challenging operating conditions. Scene segmentation and shape recognition in a single image must be carefully designed to achieve robust object detection and to facilitate object pose estimation. We describe a novel multi-feature object detection algorithm conceived to find human-made artefacts lying on the seabed. The proposed method searches for a target object according to a few general criteria that are robust to the underwater context, such as salient colour uniformity and sharp contours. We assess the performance of the proposed algorithm across different underwater datasets. The datasets have been obtained using stereo cameras of different quality, and diverge for the target object type and colour, acquisition depth and conditions. The effectiveness of the proposed approach has been experimentally demonstrated. Finally, object detection is discussed in connection with the simple colour-based segmentation and with the difficulty of tri-dimensional processing on noisy data.

Industrial AGVs: Toward a pervasive diffusion in modern factory warehouses

This paper describes the technology behind the automation of modern factory warehouses with multiple Automated Guided Vehicles (AGVs). In particular, we focus on intermediate results of Plug-and-Navigate Robots (PAN-Robots) project describing how the latest developed technologies have dealt with main issues about traffic, safety and performance of an automated warehouse. A survey about the market impact produced by first outcomes is then drafted and a roadmap to a pervasive diffusion of AGVs is finally presented.

Underwater Intervention Robotics: An Outline of the Italian National Project MARIS

The Italian national project MARIS (Marine Robotics for InterventionS) pursues the strategic objective of studying, developing and integrating technologies and methodologies enabling the development of autonomous underwater robotic systems employable for intervention activities, which are becoming progressively more typical for the underwater offshore industry, for search-and-rescue operations, and for underwater scientific missions. Within such an ambitious objective, the project consortium also intends to demonstrate the achievable operational capabilities at a proof-of-concept level, by integrating the results with prototype experimental

A low-cost stereo system for 3D object recognition

In this paper, we present a low-cost stereo vision system designed for object recognition with FPFH point feature descriptors. Image acquisition is performed using a pair of consumer market UVC cameras costing less than 80 Euros, lacking synchronization signal and without customizable optics. Nonetheless, the acquired point clouds are sufficiently accurate to perform object recognition using FPFH features. The recognition algorithm compares the point cluster extracted from the current image pair with the models contained in a dataset. Experiments show that the recognition rate is above 80% even when the object is partially occluded.

Integration of a stereo vision system into an autonomous underwater vehicle for pipe manipulation tasks

Integration of stereo vision system for detecting cylindrical pipes in autonomous underwater interventions.Vision-based object detection, pose estimation, and tracking for manipulation of submerged items.Experiments include successful underwater grasping of target pipe in different light conditions. Display Omitted Underwater object detection and recognition using computer vision are challenging tasks due to the poor light condition of submerged environments. For intervention missions requiring grasping and manipulation of submerged objects, a vision system must provide an Autonomous Underwater Vehicles (AUV) with object detection, localization and tracking capabilities. In this paper, we describe the integration of a vision system in the MARIS intervention AUV and its configuration for detecting cylindrical pipes, a typical artifact of interest in underwater operations. Pipe edges are tracked using an alpha-beta filter to achieve robustness and return a reliable pose estimation even in case of partial pipe visibility. Experiments in an outdoor water pool in different light conditions show that the adopted algorithmic approach allows detection of target pipes and provides a sufficiently accurate estimation of their pose even when they become partially visible, thereby supporting the AUV in several successful pipe grasping operations.

Guard rail detection using radar and vision data fusion for vehicle detection algorithm improvement and speed-up

This paper describes a method for detecting guard rails fusing radar and vision data in order to improve and speed-up vehicle detection algorithms. The method is based on the search for uninterrupted oblique lines that cross an interest area. The interest area is dynamically indicated by a radar sensor. A method to manage overlapping areas is also described. The methods efficiency, both in terms of time saving and correct detection rate, is numerically shown.

Improving AGV systems: Integration of advanced sensing and control technologies

This paper focuses on the integration of advanced sensing and control technologies into advanced systems of multiple Automated Guided Vehicles (AGVs). In particular, we focus on the key technologies developed within the Plug-and-Navigate Robots (PAN-Robots) project, highlighting how those results contribute to improving the performance of AGV systems.

Unsupervised Range Image Segmentation and Object Recognition Using Feature Proximity and Markov Random Field

In this paper, we propose a framework for unsupervised range image segmentation and object recognition that exploits feature similarity and proximity as leading criteria in the processing steps. Feature vectors are distinctive traits like color, texture and shape of the regions of the scene; proximity of similar features enforces classification and association decisions. Segmentation is performed by dividing the input point cloud into voxels, by extracting and clustering features from each voxel, and by refining such segmentation through Markov Random Field model. Candidate objects are selected from the resulting regions of interest and compared with the models contained in a dataset. Object recognition is performed by aligning the models with the refined point cloud clusters. Experiments show the consistency of the segmentation algorithm as well as the potential for recognition even when partial views of the object are available.

The PAN-Robots Project: Advanced Automated Guided Vehicle Systems for Industrial Logistics

In modern manufacturing plants, automation is widely adopted in the production phases, which leads to a high level of productivity and efficiency. However, the same level of automation is generally not achieved in logistics, typically performed by human operators and manually driven vehicles. In fact, even though automated guided vehicles (AGVs) have been used for a few decades for goods transportation in industrial environments [1], they do not yet represent a widespread solution and are typically applied only in specific scenarios.