Using Modelica for advanced Multi-Body modelling in 3D graphical robotic simulators

This paper describes a framework to extend the 3D robotic simulation environment Gazebo, and similar ones, with enhanced, tailor-made, multi-body dynamics specified in the Modelica language. The body-to-body interaction models are written in Modelica, but they use the sophisticated collision detection capabilities of the Gazebo engine. This contribution is a first step toward the simulation of complex robotics systems integrating detailed physics modelling and realistic sensors such as lidar and cameras. A proof-of-concept implementation is described in the paper integrating Gazebo collider and the Modelica MultiBody library, and the results obtained when simulating the interaction of an elastic sphere with a rigid plane are shown

LIFE AND WORK OF DOMENICO CANDELORO: AN APPRECIATION

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Function Spaces, Approximation Theory, and Their Applications

The purpose of this special issue was to present new developments in the theory of function spaces, along with the deep interconnections with approximation theory and the applications in various fields of pure and applied mathematics. The reaction of the mathematical community was very satisfactory. We collected thirty-five submissions, covering a wide range of mathematical topics, ten of which were found to be suitable for publications in this issue. The major part of the accepted papers treats function spaces and their applications. In this respect, in the article by X Yang et al. a new class of function spaces, named "multi-βnormed spaces", is introduced, in connection with stability properties of certain type of functional equations, while, in the paper by A. A. Bakery, sequential spaces of Orlicz type are studied and connected with the theory of summability. In the review paper by L. Angeloni and G. Vinti, the approximation theory in the space of functions with bounded variation is developed, in view of applications to signal processing. Different notions of variation are considered and several approximation theorems for families of integral or discrete type operators are given. In the more theoretical article by S. Wulede et al., a new class of Banach spaces which generalizes the class of uniformly extremely convex Banach spaces is introduced, and some characterizations of these spaces are given. Another paper by N. Khan treats the convergence of new type of double sequences, here introduced, in n-normed spaces. An interesting abstract approach to the theory of filter convergence is given in the article by A. Boccuto and X. Dimitriou, in which the links with function spaces and approximation theory are also dealt with. Other aspects of the theory of function spaces and their interconnections with calculus of variations, numerical analysis, complex variables, and stochastic processes are discussed, respectively, in the articles by T. Ma and Y. Feng, H. Wang et al., S. Wang and T. Zhan, and finally P. Duan.These four papers point out how certain methods of general approximation theory in function spaces can be employed in order to solve problems coming from a large variety of mathematical fields. We think that these contributions may represent starting points for new researches in the field of function spaces and approximation theory

Robots for Elderly Care in the Home: A Landscape Analysis and Co-Design Toolkit

Over the last two decades, several deployments of robots for in-house assistance of older adults have been trialled. However, these solutions are mostly prototypes and remain unused in real-life scenarios. In this work, we review the historical and current landscape of the field, to try and understand why robots have yet to succeed as personal assistants in daily life. Our analysis focuses on two complementary aspects: the capabilities of the physical platform and the logic of the deployment. The former analysis shows regularities in hardware configurations and functionalities, leading to the definition of a set of six application-level capabilities (exploration, identification, remote control, communication, manipulation, and digital situatedness). The latter focuses on the impact of robots on the daily life of users and categorises the deployment of robots for healthcare interventions using three types of services: support, mitigation, and response. Our investigation reveals that the value of healthcare interventions is limited by a stagnation of functionalities and a disconnection between the robotic platform and the design of the intervention. To address this issue, we propose a novel co-design toolkit, which uses an ecological framework for robot interventions in the healthcare domain. Our approach connects robot capabilities with known geriatric factors, to create a holistic view encompassing both the physical platform and the logic of the deployment. As a case study-based validation, we discuss the use of the toolkit in the pre-design of the robotic platform for an pilot intervention, part of the EU large-scale pilot of the EU H2020 GATEKEEPER project

Task-Agnostic Object Recognition for Mobile Robots through Few-Shot Image Matching

To assist humans with their daily tasks, mobile robots are expected to navigate complex and dynamic environments, presenting unpredictable combinations of known and unknown objects. Most state-of-the-art object recognition methods are unsuitable for this scenario because they require that: (i) all target object classes are known beforehand, and (ii) a vast number of training examples is provided for each class. This evidence calls for novel methods to handle unknown object classes, for which fewer images are initially available (few-shot recognition). One way of tackling the problem is learning how to match novel objects to their most similar supporting example. Here, we compare different (shallow and deep) approaches to few-shot image matching on a novel data set, consisting of 2D views of common object types drawn from a combination of ShapeNet and Google. First, we assess if the similarity of objects learned from a combination of ShapeNet and Google can scale up to new object classes, i.e., categories unseen at training time. Furthermore, we show how normalising the learned embeddings can impact the generalisation abilities of the tested methods, in the context of two novel configurations: (i) where the weights of a Convolutional two-branch Network are imprinted and (ii) where the embeddings of a Convolutional Siamese Network are L2-normalised

Fit to Measure: Reasoning about Sizes for Robust Object Recognition

Service robots can help with many of our daily tasks, especially in those cases where it is inconvenient orunsafe for us to intervene – e.g., under extreme weather conditions or when social distance needs to bemaintained. However, before we can successfully delegate complex tasks to robots, we need to enhancetheir ability to make sense of dynamic, real-world environments. In this context, the first prerequisite toimproving the Visual Intelligence of a robot is building robust and reliable object recognition systems.While object recognition solutions are traditionally based on Machine Learning, augmenting them withknowledge-based reasoners has been shown to improve their performance. In particular, based on ourprior work on identifying the epistemic requirements of Visual Intelligence, we hypothesise that knowl-edge of the typical size of objects can significantly improve the accuracy of an object recognition system.To verify this hypothesis, in this paper we present an approach to integrating knowledge about objectsizes in a ML-based architecture. Our experiments in a real-world robotic scenario show that this hybridapproach ensures a significant performance increase over state-of-the-art Machine Learning methods

Exploring Task-agnostic, ShapeNet-based Object Recognition for Mobile Robots

This position paper presents an attempt to improve the scalability of existing object recognition methods, which largely rely on supervision and imply a huge availability of manually-labelled data points. Moreover, in the context of mobile robotics, data sets and experimental settings are often handcrafted based on the specific task the object recognition is aimed at, e.g. object grasping. In this work, we argue instead that publicly available open data such as ShapeNet can be used for object classification first, and then to link objects to their related concepts, leading to task-agnostic knowledge acquisition practices. To this aim, we evaluated five pipelines for object recognition, where target classes were all entities collected from ShapeNet and matching was based on: (i) shape-only features, (ii) RGB histogram comparison, (iii) a combination of shape and colour matching, (iv) image feature descriptors, and (v) inexact, normalised cross-correlation, resembling the Deep, Siamese-like NN architecture of Submariam et al. (2016). We discussed the relative impact of shape-derived and colour-derived features, as well as suitability of all tested solutions for future application to real-life use cases

Introducing a Smart City Component in a Robotic Competition: A Field Report

In recent years, two fields have become more prominent in our everyday life: smart cities and service robots. In a smart city, information is collected from distributed sensors around the city into centralised data hubs and used to improve the efficiency of the city systems and provide better services to citizens. Exploiting major advances in Computer Vision and Machine Learning, service robots have evolved from performing simple tasks to playing the role of hotel concierges, museum guides, waiters in cafes and restaurants, home assistants, automated delivery drones, and more. As digital agents, robots can be prime members of the smart city vision. On the one hand, smart city data can be accessed by robots to gain information that is relevant to the task in hand. On the other hand, robots can act as mobile sensors and actuators on behalf of the smart city, thus contributing to the data acquisition process. However, the connection between service robots and smart cities is surprisingly under-explored. In an effort to stimulate advances on the integration between robots and smart cities, we turned to robot competitions and hosted the first Smart Cities Robotics Challenge (SciRoc). The contest included activities specifically designed to require cooperation between robots and the MK Data Hub, a Smart City data infrastructure. In this article, we report on the competition held in Milton Keynes (UK) in September 2019, focusing in particular on the role played by the MK Data Hub in simulating a Smart City Data Infrastructure for service robots. Additionally, we discuss the feedback we received from the various people involved in the SciRoc Challenge, including participants, members of the public and organisers, and summarise the lessons learnt from this experience

From Models to Software Through Automatic Transformations: An AADL to ROS End-to-End Toolchain

Modelling complex systems is a common practice and de facto standard across most application domains in engineering. Although it would seem unreasonable - and quite impractical - to build a structure as complex as a bridge without a reference blueprint detailing how to arrange all of its building blocks, in Software Development, and, particularly in the context of Robotics, examples adhering to rigorous modelling routines are still relatively rare to find. Yet, models help understanding complex problems while pinpointing their potential solutions, through abstraction. Further, models aid communication, i.e., the unambiguous exchange of reasoning processes across the involved agents. The complexity of Robotic Software Systems suggests that a widespread application of modelling techniques, from the very initial implementation stages, would (i) ease the definition, engineering and debugging of the related sub-features significantly, and (ii) guide collaborative efforts towards a common standard. To this aim, we presented a toolchain conceived for parsing an input AADL model into a compilable code suite. Keeping the model building and the linkage of the robot application with the ROS environment in the developer's hands, this framework delegates all the remaining tasks to an automated code generator, producing a fully-functioning ROS packages (i.e., already configured and ready for compiling) as output. We first presented the discussed framework, highlighted its related advantages - when compared to the only other similar approach found in the literature -, and used it as an exemplary use case, to prompt broader discussions on the benefits of model-based software development in Robotics

Using AADL to model and develop ROS-based robotic application

Modern robotic systems are a combination of sophisticated software and hardware components and they offer complex functionalities. While popular middlewares that promote component-level reusability and assist development already exist, there are no established techniques or procedures that use a formal approach to robot system and architecture design yet. This work aims at the long term goal of model-based design and development of complex robot systems (and their software architectures), by surpassing current techniques based on personal expertise, and best practices, in favor of purely model-based approaches. Our contribution tackles the problem from the ground up by proposing a way to model ROS nodes, and robotic architectures in general, using the Architecture Analysis and Design Language (AADL). The result is connected to and based on ROS, but not bound to it. It provides a starting point for the future definition of a general formal framework to describe complex robotic architectures suitable for automatic code generation and system verification