Vision based robot-to-robot object handover

This paper presents an autonomous robot-to-robot object handover in the presence of uncertainties and in the absence of explicit communication. Both the giver and receiver robots are equipped with an eye-in-hand depth camera. The object to handle is roughly positioned in the field of view of the giver robot's camera and a deep learning based approach is adopted for detecting the object. The physical exchange is performed by recurring to an estimate of the contact forces and an impedance control, which allows the receiver robot to perceive the presence of the object and the giver one to recognize that the handover is complete. Experimental results, conducted on a couple of collaborative 7 DoF manipulators in a partially structured environment, demonstrate the effectiveness of the proposed approach

Data Flow ORB-SLAM for Real-time Performance on Embedded GPU Boards

The use of embedded boards on robots, including unmanned aerial and ground vehicles, is increasing thanks to the availability of GPU equipped low-cost embedded boards in the market. Porting algorithms originally designed for desktop CPUs on those boards is not straightforward due to hardware limitations. In this paper, we present how we modified and customized the open source SLAM algorithm ORB-SLAM2 to run in real-time on the NVIDIA Jetson TX2. We adopted a data flow paradigm to process the images, obtaining an efficient CPU/GPU load distribution that results in a processing speed of about 30 frames per second. Quantitative experimental results on four different sequences of the KITTI datasets demonstrate the effectiveness of the proposed approach. The source code of our data flow ORB-SLAM2 algorithm is publicly available on GitHub

Multivariate sensor signals collected by aquatic drones involved in water monitoring: A complete dataset

Sensor data generated by intelligent systems, such as autonomous robots, smart buildings and other systems based on artificial intelligence, represent valuable sources of knowledge in today's data-driven society, since they contain information about the situations these systems face during their operation. These data are usually multivariate time series since modern technologies enable the simultaneous acquisition of multiple signals during long periods of time. In this paper we present a dataset containing sensor traces of six data acquisition campaigns performed by autonomous aquatic drones involved in water monitoring. A total of 5.6 h of navigation are available, with data coming from both lakes and rivers, and from different locations in Italy and Spain. The monitored variables concern both the internal state of the drone (e.g., battery voltage, GPS position and signals to propellers) and the state of the water (e.g., temperature, dissolved oxygen and electrical conductivity). Data were collected in the context of the EU-funded Horizon 2020 project INTCATCH (http://www.intcatch.eu) which aims to develop a new paradigm for monitoring water quality of catchments. The aquatic drones used for data acquisition are Platypus Lutra boats. Both autonomous and manual drive is used in different parts of the navigation. The dataset is analyzed in the paper “Time series segmentation for state-model generation of autonomous aquatic drones: A systematic framework” [1] by means of recent time series clustering/segmentation techniques to extract data-driven models of the situations faced by the drones in the data acquisition campaigns. These data have strong potential for reuse in other kinds of data analysis and evaluation of machine learning methods on real-world datasets [2]. Moreover, we consider this dataset valuable also for the variety of situations faced by the drone, from which machine learning techniques can learn behavioral patterns or detect anomalous activities. We also provide manual labeling for some known states of the drones, such as, drone inside/outside the water, upstream/downstream navigation, manual/autonomous drive, and drone turning, that represent a ground truth for validation purposes. Finally, the real-world nature of the dataset makes it more challenging for machine learning methods because it contains noisy samples collected while the drone was exposed to atmospheric agents and uncertain water flow conditions

A distributed vision system for boat traffic monitoring in the venice grand canal

Motion detection and Tracking, Distribuited surveillance, Boat traffic monitoring In this paper we describe a system for boat traffic monitoring that has been realized for analyzing and computing statistics of trafic in the Grand Canal in Venice. The system is based on a set of survey cells to monitor about 6 Km of canal. Each survey cell contains three cameras oriented in three directions and covering about 250-300 meters of the canal. This paper presents the segmentation and tracking phases that are used to detect and track boats in the channel and experimental evaluation of the system showing the effectiveness of the approach in the required tasks.

Deep learning-based pixel-wise lesion segmentation on oral squamous cell carcinoma images

Oral squamous cell carcinoma is the most common oral cancer. In this paper, we present a performance analysis of four different deep learning-based pixel-wise methods for lesion segmentation on oral carcinoma images. Two diverse image datasets, one for training and another one for testing, are used to generate and evaluate the models used for segmenting the images, thus allowing to assess the generalization capability of the considered deep network architectures. An important contribution of this work is the creation of the Oral Cancer Annotated (ORCA) dataset, containing ground-truth data derived from the well-known Cancer Genome Atlas (TCGA) dataset

Multi-Agent Coordination for a Partially Observable and Dynamic Robot Soccer Environment with Limited Communication

RoboCup represents an International testbed for advancing research in AI and robotics, focusing on a definite goal: developing a robot team that can win against the human world soccer champion team by the year 2050. To achieve this goal, autonomous humanoid robots' coordination is crucial. This paper explores novel solutions within the RoboCup Standard Platform League (SPL), where a reduction in WiFi communication is imperative, leading to the development of new coordination paradigms. The SPL has experienced a substantial decrease in network packet rate, compelling the need for advanced coordination architectures to maintain optimal team functionality in dynamic environments. Inspired by market-based task assignment, we introduce a novel distributed coordination system to orchestrate autonomous robots' actions efficiently in low communication scenarios. This approach has been tested with NAO robots during official RoboCup competitions and in the SimRobot simulator, demonstrating a notable reduction in task overlaps in limited communication settings.Comment: International Conference of the Italian Association for Artificial Intelligence (AIxIA 2023) - Italian Workshop on Artificial Intelligence and Robotics (AIRO) Rome, 6 - 9 November, 202

Skin Lesion Area Segmentation Using Attention Squeeze U-Net for Embedded Devices

Melanoma is the deadliest form of skin cancer. Early diagnosis of malignant lesions is crucial for reducing mortality. The use of deep learning techniques on dermoscopic images can help in keeping track of the change over time in the appearance of the lesion, which is an important factor for detecting malignant lesions. In this paper, we present a deep learning architecture called Attention Squeeze U-Net for skin lesion area segmentation specifically designed for embedded devices. The main goal is to increase the patient empowerment through the adoption of deep learning algorithms that can run locally on smartphones or low cost embedded devices. This can be the basis to (1) create a history of the lesion, (2) reduce patient visits to the hospital, and (3) protect the privacy of the users. Quantitative results on publicly available data demonstrate that it is possible to achieve good segmentation results even with a compact model

Vision-enhanced Peg-in-Hole for automotive body parts using semantic image segmentation and object detection

Artificial Intelligence (AI) is an enabling technology in the context of Industry 4.0. In particular, the automotive sector is among those who can benefit most of the use of AI in conjunction with advanced vision techniques. The scope of this work is to integrate deep learning algorithms in an industrial scenario involving a robotic Peg-in-Hole task. More in detail, we focus on a scenario where a human operator manually positions a carbon fiber automotive part in the workspace of a 7 Degrees of Freedom (DOF) manipulator. To cope with the uncertainty on the relative position between the robot and the workpiece, we adopt a three stage strategy. The first stage concerns the Three-Dimensional (3D) reconstruction of the workpiece using a registration algorithm based on the Iterative Closest Point (ICP) paradigm. Such a procedure is integrated with a semantic image segmentation neural network, which is in charge of removing the background of the scene to improve the registration. The adoption of such network allows to reduce the registration time of about 28.8%. In the second stage, the reconstructed surface is compared with a Computer Aided Design (CAD) model of the workpiece to locate the holes and their axes. In this stage, the adoption of a Convolutional Neural Network (CNN) allows to improve the holes’ position estimation of about 57.3%. The third stage concerns the insertion of the peg by implementing a search phase to handle the remaining estimation errors. Also in this case, the use of the CNN reduces the search phase duration of about 71.3%. Quantitative experiments, including a comparison with a previous approach without both the segmentation network and the CNN, have been conducted in a realistic scenario. The results show the effectiveness of the proposed approach and how the integration of AI techniques improves the success rate from 84.5% to 99.0%

Laser cleaning of gilded wood: a comparative study of colour variations induced by irradiation at different wavelengths

There is a growing interest by art conservators for laser cleaning of wood artworks, since traditional cleaning with chemical solvents can be a source of decay, due to the prolonged action of chemicals after the restoration. In this experiment we used excimer and Nd:YAGlasers, emitting radiation in the ultraviolet (248 nm), visible (532 nm) and near infrared (1064 nm), to investigate the effect of laser interaction on gilded wood samples at different wavelengths