Ferruccio Damiani

Multi-robot systems (MRS) are becoming increasingly relevant within diverse application domains, including warehouse logistics, where robots are in charge of moving and storing crates, by navigating through the warehouse shelves. Engineering such robust multi-robot systems is a significant challenge, especially if the goal is to build a fully decentralized system, without single points of failure, or potential bottlenecks. Aggregate Programming (AP) is an established approach to engineering resilient distributed systems, which has already been applied successfully for building prototypes in Industrial Internet of Things and robotics scenarios. In this paper we present an in depth description of how we have exploited AP for designing and simulating a MRS to address a non-trivial farm warehouse logistics case study proposed by a company operating in the Autonomous Mobile Robots (AMR) market, which cooperated in this research. Importantly, to better validate the implemented prototype, we have relied on two different levels of simulation, one scalable and high-level, and another one small-scale but highly realistic.

Distributed runtime verification (DRV) addresses the problem of checking the correctness of distributed systems during execution, coping with partial knowledge, dynamic topologies, and the absence of global time. These challenges are particularly prominent in proximity-based networks, such as those arising in IoT and Far Edge computing scenarios, where large numbers of devices interact through local communication. This tutorial presents an approach to DRV based on Aggregate Programming (AP), a paradigm for designing distributed collective systems via high-level abstractions over computational fields. We show how temporal and spatial properties (expressed in past-CTL and SLCS, respectively) can be systematically compiled into aggregate monitors grounded in the eXchange Calculus and executed using the FCPP C++ framework and simulator for AP. The tutorial combines conceptual foundations with practical guidance: participants learn how to specify spatio-temporal properties, generate corresponding monitors, and execute them in a 3D simulation environment. Examples are drawn from ongoing industrial collaborations and research projects, which we use to illustrate realistic monitoring scenarios and motivate open challenges for AP-based DRV.

The paper offers a critical-historical analysis of Palazzo Tutinelli and Piazza Garibaldi in Atina (FR), focused on knowledge — understood as critical history — and on intervention at the urban scale. Through a diachronic reading of iconographic and documentary sources (nineteenth-century photographs, 1951 reconstruction drawings, 1984 post-seismic reports and contemporary digital surveys), the transformative phases of the complex are reconstructed: from its nineteenth-century configuration through the wartime devastation of 1943–1944 to the post-war reconstruction and seismic strengthening. The Porta dell’Assunta, pivot of the frontage, is read as an urban threshold and as a device of collective memory. Systematic knowledge is argued to be the precondition of any sustainable conservation strategy.

Improved autonomy in robotic systems is needed for innovation in, e.g., the marine sector. Autonomous robots that are let loose in hazardous environments, such as underwater, need to handle uncertainties that stem from both their environment and internal state. While self-adaptation is crucial to cope with these uncertainties, bad decisions may cause the robot to get lost or even to cause severe environmental damage. Autonomous, self-adaptive robots that operate in uncontrolled environments full of uncertainties need to be reliable! Since these uncertainties are hard to replicate in test deployments, we need methods to formally analyse self-adaptive robots operating in uncontrolled environments. In this article, we show how feature-oriented techniques can be used to formally model and analyse self-adaptive robotic systems in the presence of such uncertainties. Self-adaptive systems can be organised as two-layered systems with a managed subsystem handling the domain concerns and a managing subsystem implementing the adaptation logic. We consider a case study of an Autonomous Underwater Vehicle (AUV) for pipeline inspection, in which the managed subsystem of the AUV is modelled as a family of systems, where each family member corresponds to a valid configuration of the AUV which can be seen as an operating mode of the AUV’s behaviour. The managing subsystem of the AUV is modelled as a control layer that is capable of dynamically switching between such valid configurations, depending on both environmental and internal uncertainties. These uncertainties are captured in a probabilistic and highly configurable model. Our modelling approach allows us to exploit powerful formal methods for feature-oriented systems, which we illustrate by analysing safety properties, energy consumption, and multi-objective properties, as well as performing parameter synthesis to analyse to what extent environmental conditions affect the AUV. The case study is realised in the probabilistic feature-oriented modelling language and verification tool ProFeat, and in particular exploits family-based probabilistic and parametric model checking.

Recent trends like the Internet of Things (IoT) suggest a vision of dense and multi-scale deployments of computing devices in nearly all kinds of environments. A prominent engineering challenge revolves around programming the collective adaptive behaviour of such computational ecosystems. This requires abstractions able to capture concepts like ensembles (dynamic groups of cooperating devices) and collective tasks (joint activities carried out by ensembles). In this work, we consider collections of devices interacting with neighbours and that execute in nearly-synchronised sense-compute-interact rounds, where the computation is given by a single program mapping sensing values and incoming messages to output and outcoming messages. To support programming whole computational collectives, we propose the abstraction of a distributed collective process, which can be used to define at once the ensemble formation logic and its collective task. We formalise the abstraction in the eXchange Calculus (XC), a core functional language based on neighbouring values (maps from neighbours to values) where state and interaction is handled through a single primitive, exchange, and provide a corresponding implementation in the FCPP language. Then, we exercise distributed collective processes using two case studies: multi-hop message propagation and distributed monitoring of spatial properties. Finally, we discuss the features of the abstraction and its suitability for different kinds of distributed computing applications.

Today’s distributed and pervasive computing addresses large-scale cyber-physical ecosystems, characterised by dense and large networks of devices capable of computation, communication and interaction with the environment and people. While most research focuses on treating these systems as ‘composites’ (i.e., heterogeneous functional complexes), recent developments in fields such as self-organising systems and swarm robotics have opened up a complementary perspective: treating systems as ‘collectives’ (i.e., uniform, collaborative and self-organising groups of entities). This article explores the motivations, state of the art and implications of this ‘collective computing paradigm’ in software engineering. In particular, it discusses its peculiar challenges, implied by characteristics like distribution, situatedness, large scale and cooperative nature. These challenges outline significant directions for future research in software engineering, touching on aspects such as macro-programming, collective intelligence, self-adaptive middleware, learning/synthesis of collective behaviour, human involvement, safety and security in collective cyber-physical ecosystems.