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Fulvio Mastrogiovanni

Publications and source records attributed to Fulvio Mastrogiovanni.

86 records · Page 5Linked to original sources

Long-term area coverage and radio relay positioning using swarms of UAVs

Unmanned Aerial Vehicles (UAVs) are becoming increasingly useful for tasks which require the acquisition of data over large areas. The coverage problem, i.e., the problem of periodically visiting all subregions of an area at a desired frequency, is especially interesting because of its practical applications, both in industry and long-term monitoring of areas hit by a natural disaster. We focus here on the latter scenario, and take into consideration its peculiar characteristic, i.e. the a coverage system should be resilient to a changing environment and not be dependent on pre-existing infrastructures for communication. To this purpose, we designed a novel algorithm for online area coverage and simultaneous signal relay that allows a UAV to cover an area freely, while a variable number of other UAVs provide a stable communication with the base and support in the coverage process at the same time. Finally, a test architecture based on the algorithm has been developed and tests have been performed. By comparison with a simple relay chain system, our approach employs up to 64% less time to reach a certain goal of coverage iterations over the map with only 17% worse average communication cost and no impact on the worst case communication cost.

cs.RO↗

Online Human Gesture Recognition using Recurrent Neural Networks and Wearable Sensors

Gestures are a natural communication modality for humans. The ability to interpret gestures is fundamental for robots aiming to naturally interact with humans. Wearable sensors are promising to monitor human activity, in particular the usage of triaxial accelerometers for gesture recognition have been explored. Despite this, the state of the art presents lack of systems for reliable online gesture recognition using accelerometer data. The article proposes SLOTH, an architecture for online gesture recognition, based on a wearable triaxial accelerometer, a Recurrent Neural Network (RNN) probabilistic classifier and a procedure for continuous gesture detection, relying on modelling gesture probabilities, that guarantees (i) good recognition results in terms of precision and recall, (ii) immediate system reactivity.

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Arianna+: Scalable Human Activity Recognition by Reasoning with a Network of Ontologies

Aging population ratios are rising significantly. Meanwhile, smart home based health monitoring services are evolving rapidly to become a viable alternative to traditional healthcare solutions. Such services can augment qualitative analyses done by gerontologists with quantitative data. Hence, the recognition of Activities of Daily Living (ADL) has become an active domain of research in recent times. For a system to perform human activity recognition in a real-world environment, multiple requirements exist, such as scalability, robustness, ability to deal with uncertainty (e.g., missing sensor data), to operate with multi-occupants and to take into account their privacy and security. This paper attempts to address the requirements of scalability and robustness, by describing a reasoning mechanism based on modular spatial and/or temporal context models as a network of ontologies. The reasoning mechanism has been implemented in a smart home system referred to as Arianna+. The paper presents and discusses a use case, and experiments are performed on a simulated dataset, to showcase Arianna+'s modularity feature, internal working, and computational performance. Results indicate scalability and robustness for human activity recognition processes.

cs.AI↗

Contact modelling and tactile data processing for robot skin

Tactile sensing is a key enabling technology to develop complex behaviours for robots interacting with humans or the environment. This paper discusses computational aspects playing a significant role when extracting information about contact events. Considering a large-scale, capacitance-based robot skin technology we developed in the past few years, we analyse the classical Boussinesq-Cerruti's solution and the Love's approach for solving a distributed inverse contact problem, both from a qualitative and a computational perspective. Our contribution is the characterisation of algorithms performance using a freely available dataset and data originating from surfaces provided with robot skin.

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A framework for Culture-aware Robots based on Fuzzy Logic

Cultural adaptation, i.e., the matching of a robot's behaviours to the cultural norms and preferences of its user, is a well known key requirement for the success of any assistive application. However, culture-dependent robot behaviours are often implicitly set by designers, thus not allowing for an easy and automatic adaptation to different cultures. This paper presents a method for the design of culture-aware robots, that can automatically adapt their behaviour to conform to a given culture. We propose a mapping from cultural factors to related parameters of robot behaviours which relies on linguistic variables to encode heterogeneous cultural factors in a uniform formalism, and on fuzzy rules to encode qualitative relations among multiple variables. We illustrate the approach in two practical case studies.

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On the manipulation of articulated objects in human-robot cooperation scenarios

Articulated and flexible objects constitute a challenge for robot manipulation tasks but are present in different real-world settings, including home and industrial environments. Current approaches to the manipulation of articulated and flexible objects employ ad hoc strategies to sequence and perform actions on them depending on a number of physical or geometrical characteristics related to those objects, as well as on an a priori classification of target object configurations. In this paper, we propose an action planning and execution framework, which (i) considers abstract representations of articulated or flexible objects, (ii) integrates action planning to reason upon such configurations and to sequence an appropriate set of actions with the aim of obtaining a target configuration provided as a goal, and (iii) is able to cooperate with humans to collaboratively carry out the plan. On the one hand, we show that a trade-off exists between the way articulated or flexible objects are perceived and how the system represents them. Such a trade-off greatly impacts on the complexity of the planning process. On the other hand, we demonstrate the system's capabilities in allowing humans to interrupt robot action execution, and - in general - to contribute to the whole manipulation process. Results related to planning performance are discussed, and examples of a Baxter dual-arm manipulator performing actions collaboratively with humans are shown.

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The robot skin placement problem: a new technique to place triangular modules inside polygons

Providing robots with large-scale robot skin is a challenging goal, especially when considering surfaces characterized by different shapes and curvatures. The problem originates from technological advances in tactile sensing, and in particular from two requirements: (i) covering the largest possible area of a robot's surface with tactile sensors, and (ii) doing it using cheap, replicable hardware modules. Given modules of a specific shape, the problem of optimally placing them requires to maximize the number of modules that can be fixed on the selected robot body part. Differently from previous approaches, which are based on methods inspired by computational geometry (e.g., packing), we propose a novel layout design method inspired by physical insights, referred to as Iterative Placement (ItPla), which arranges modules as if physical forces acted on them. A number of case studies from the literature are considered to evaluate the algorithm.

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Towards a new paradigm for assistive technology at home: research challenges, design issues and performance assessment

Providing elderly and people with special needs, including those suffering from physical disabilities and chronic diseases, with the possibility of retaining their independence at best is one of the most important challenges our society is expected to face. Assistance models based on the home care paradigm are being adopted rapidly in almost all industrialized and emerging countries. Such paradigms hypothesize that it is necessary to ensure that the so-called Activities of Daily Living are correctly and regularly performed by the assisted person to increase the perception of an improved quality of life. This chapter describes the computational inference engine at the core of Arianna, a system able to understand whether an assisted person performs a given set of ADL and to motivate him/her in performing them through a speech-mediated motivational dialogue, using a set of nearables to be installed in an apartment, plus a wearable to be worn or fit in garments.

cs.AI↗

Scene learning, recognition and similarity detection in a fuzzy ontology via human examples

This paper introduces a Fuzzy Logic framework for scene learning, recognition and similarity detection, where scenes are taught via human examples. The framework allows a robot to: (i) deal with the intrinsic vagueness associated with determining spatial relations among objects; (ii) infer similarities and dissimilarities in a set of scenes, and represent them in a hierarchical structure represented in a Fuzzy ontology. In this paper, we briefly formalize our approach and we provide a few use cases by way of illustration. Nevertheless, we discuss how the framework can be used in real-world scenarios.

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Node Primitives: an open end-user programming platform for social robots

With the expected adoption of robots able to seamlessly and intuitively interact with people in real-world scenarios, the need arises to provide non-technically-skilled users with easy-to-understand paradigms for customising robot behaviors. In this paper, we present an interaction design robot programming platform for enabling multidisciplinary social robot research and applications. This platform is referred to Node Primitives (NEP) and consists of two main parts. On the one hand, a ZeroMQ and Python-based distributed software framework has been developed to provide inter-process communication and robot behavior specification mechanisms. On the other hand, a web-based end-user programming (EUP) interface has been developed to allow for an easy and intuitive way of programming and executing robot behaviors. In order to evaluate NEP, we discuss the development of a human-robot interaction application using arm gestures to control robot behaviors. A usability test for the proposed EUP interface is also presented.

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Arianna: towards a new paradigm for assistive technology at home

Providing elderly and people with special needs to retain their independence as long as possible is one of the biggest challenges of the society of tomorrow. Teseo, a startup company spinoff from the University of Genoa, aims at accelerating the transition towards a sustainable healthcare system. Teseo's first concept and product, Arianna, allows for the automated recognition of activities of daily living at home and acts as a wellbeing and healthcare personalized assistant. This abstract outlines the main concepts underlying its features and capabilities.

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Flexible human-robot cooperation models for assisted shop-floor tasks

The Industry 4.0 paradigm emphasizes the crucial benefits that collaborative robots, i.e., robots able to work alongside and together with humans, could bring to the whole production process. In this context, an enabling technology yet unreached is the design of flexible robots able to deal at all levels with humans' intrinsic variability, which is not only a necessary element for a comfortable working experience for the person but also a precious capability for efficiently dealing with unexpected events. In this paper, a sensing, representation, planning and control architecture for flexible human-robot cooperation, referred to as FlexHRC, is proposed. FlexHRC relies on wearable sensors for human action recognition, AND/OR graphs for the representation of and reasoning upon cooperation models, and a Task Priority framework to decouple action planning from robot motion planning and control.

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Detection of bimanual gestures everywhere: why it matters, what we need and what is missing

Bimanual gestures are of the utmost importance for the study of motor coordination in humans and in everyday activities. A reliable detection of bimanual gestures in unconstrained environments is fundamental for their clinical study and to assess common activities of daily living. This paper investigates techniques for a reliable, unconstrained detection and classification of bimanual gestures. It assumes the availability of inertial data originating from the two hands/arms, builds upon a previously developed technique for gesture modelling based on Gaussian Mixture Modelling (GMM) and Gaussian Mixture Regression (GMR), and compares different modelling and classification techniques, which are based on a number of assumptions inspired by literature about how bimanual gestures are represented and modelled in the brain. Experiments show results related to 5 everyday bimanual activities, which have been selected on the basis of three main parameters: (not) constraining the two hands by a physical tool, (not) requiring a specific sequence of single-hand gestures, being recursive (or not). In the best performing combination of modeling approach and classification technique, five out of five activities are recognized up to an accuracy of 97%, a precision of 82% and a level of recall of 100%.

cs.CV↗

Visuospatial Skill Learning for Robots

A novel skill learning approach is proposed that allows a robot to acquire human-like visuospatial skills for object manipulation tasks. Visuospatial skills are attained by observing spatial relationships among objects through demonstrations. The proposed Visuospatial Skill Learning (VSL) is a goal-based approach that focuses on achieving a desired goal configuration of objects relative to one another while maintaining the sequence of operations. VSL is capable of learning and generalizing multi-operation skills from a single demonstration, while requiring minimum prior knowledge about the objects and the environment. In contrast to many existing approaches, VSL offers simplicity, efficiency and user-friendly human-robot interaction. We also show that VSL can be easily extended towards 3D object manipulation tasks, simply by employing point cloud processing techniques. In addition, a robot learning framework, VSL-SP, is proposed by integrating VSL, Imitation Learning, and a conventional planning method. In VSL-SP, the sequence of performed actions are learned using VSL, while the sensorimotor skills are learned using a conventional trajectory-based learning approach. such integration easily extends robot capabilities to novel situations, even by users without programming ability. In VSL-SP the internal planner of VSL is integrated with an existing action-level symbolic planner. Using the underlying constraints of the task and extracted symbolic predicates, identified by VSL, symbolic representation of the task is updated. Therefore the planner maintains a generalized representation of each skill as a reusable action, which can be used in planning and performed independently during the learning phase. The proposed approach is validated through several real-world experiments.

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