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Francesco Amigoni

The research activity of Francesco Amigoni sits at the intersection of the fields of autonomous robotics and artificial intelligence and, specifically, deals with multiagent systems. It addresses the study, development, and experimental assessment of models and algorithms for decision-making of systems composed of autonomous agents operating in the real world.

The research activities reported below have been conducted in cooperation with students and researchers, prominently including: Davide Azzalini, Jacopo Banfi, Nicola Basilico, Vincenzo Caglioti, Andrea Castelletti, Giulio Fontana, Nicola Gatti, Maria Gini, Matteo Giuliani, Michèle Lavagna, Matteo Luperto, Alberto Quattrini Li, Ioannis Rekleitis, Alessandro Riva, Viola Schiaffonati, and Marco Somalvico.

Current Research Interests

The current research topics of Francesco Amigoni are articulated in the following areas ([] refers to the main publications in the Publication List of the Curriculum Vitae).

Autonomous Mobile Robotics

Navigation Strategies


The research in this topic aims at defining efficient navigation strategies for autonomous mobile robots. Navigation strategies are methods used by mobile robots, either operating individually or organized in multirobot systems, to autonomously decide where to go when performing a task [B11] (in contrast to classical path planning methods that look for how to reach given locations, see below). Francesco Amigoni has significantly contributed to the theoretical and practical development of navigation strategies, especially for the exploration and patrolling tasks.


When exploring initially unknown environments in order to discover their geometrical features, exploration strategies determine the next observation locations mobile robots should reach in a partially known environment. Francesco Amigoni has initially proposed an information-based criterion for defining an exploration strategy for a mobile robot equipped with a laser range finder [A19]. Moreover, Francesco Amigoni has proposed decision-theoretical exploration strategies that exploit multi-objective optimization [C26] and multi-criteria decision making [A22] [C49]. An exploration strategy that, differently of the mainstream approaches, exploits not only metric information but also semantic information to determine the next observation locations has been proposed [A27], while an exploration strategy that exploits prior knowledge that may be available on the environments is described in [A41]. An exploration strategy that chooses the locations to reach also according to the predicted observations obtainable from there is proposed in [C103].

Exploration strategies have an intrinsic online nature and their performance can be compared with that of optimal offline exploration paths. An approach to find the optimal exploration paths for covering arbitrary environments is presented in [C60], while suboptimal covering paths can be found with the method of [C79] for generic environments and with that of [C100] for modular environments. Some bounds on performance of exploration strategies are reported in [C70]. Experimental comparisons of different exploration strategies in different settings are reported in [C39] [C51] [C56] [C63]. The relation between exploration strategies (where to go) and coordination methods (who goes where) in multirobot exploration has been analyzed in [C58].

Multirobot exploration becomes more challenging when robots cannot always communicate with each other [A30]. Different strategies for communication-constrained exploration are compared in [C75], while in [A34] a multirobot exploration strategy is proposed that deals with recurrent connectivity constraints. The methods proposed in [A40] build communication maps that can be used to deploy robots in communication-constrained environments, even during the exploration of the same environments [C92]. [A36] presents an approach to efficiently reconnect robots if they move out of each other’s communication ranges. The more abstract problem of collecting joint measurements from locations between which communication is possible in addressed in [C89]. Switching between different communication modalities to efficient explore environments is investigated in [C83].

Exploration of environments with mobile robots to find gas sources is discussed in [C82].


In patrolling, mobile robots move in an environment to prevent intrusions. An efficient strategy for a mobile robot that is tasked to patrol an environment is presented in [C42]. The strategy has been developed considering patrolling as a strategic game played by the mobile robot and the intruder. Following the same approach, a more mature model for finding optimal randomized patrolling strategies is reported in [C44] [C45] and extended in [C46]. A deterministic patrolling strategy is developed in [C47], while a deterministic capture strategy is described in [C90]. A step toward the application of the approach to real mobile robots is described in [C50], an application to active patrolling cameras is reported in [C53], and an application to the problem of pursuit evasion is illustrated in [C57]. Most of the above results are summarized and applied to realistically-sized environments in [A23].

Two strategies for multiple robots patrolling environments with constraints on communication are reported in [C72] and in [C78], while a multirobot system that tracks several targets is described in [A37].


Multirobot Path Planning


In the classical problem of path planning, robots autonomously plan how to reach given goal locations. Francesco Amigoni has contributed to show that this problem turns out to be computationally difficult for multirobot systems [A29] [C86] and for single robots [C96] [A39] in presence of constraints on communication and to propose solving algorithms. There are many real-life applications where several autonomous agents must coordinate to reach their goals without collisions while optimizing some performance measure. Solutions to this Multi-Agent Path Finding (MAPF) problem usually assume environments to be static and known in advance. [C99] introduces C-MAPF, a novel variant of the MAPF problem in which the environment is configurable and its structure and topology can be modified within some given constraints. Other variants address the case in which more teams of robots operate in the same environment [C106] and exploit the knowledge about the appearance of future tasks [C107].


Semantic Maps and Map Prediction


Semantic maps associate high-level information (e.g., labels like ‘kitchen’) to locations of environments. Francesco Amigoni has addressed different aspects involved in building semantic maps of indoor environments considering a priori knowledge about the type of the building in which robots are operating in [C62] [C68] [C74] [C80]. In [A38] a method able to predict the structure of buildings of a given type is described, while the method in [A45] reconstructs the geometry of parts of buildings that have not been observed and the method in [A46] predicts the geometry of rooms behind closed doors. The identification of the structure of buildings starting from observations made by robots is addressed in [A44].


Anomaly Detection for Autonomous Robots


Detection of anomalies and faults is a key element for long-term robot autonomy, because, together with subsequent diagnosis and recovery, allows the robots to reach the required levels of robustness and persistency. In [C98] and [A42], Francesco Amigoni has proposed approaches based on HMMs (Hidden Markov Models) and on neural networks, respectively, for detecting anomalous behaviors in autonomous robots starting from data collected during their routine operations.


Experimental Methodologies and Standards


The research in this topic aims at improving experimentation in autonomous mobile robotics, which has not yet reached a level of maturity comparable to that reached in other scientific and engineering disciplines [B10]. Francesco Amigoni has been among the firsts to contribute to the definition of good experimental methodologies for robotic mapping [C37] and to propose a general theoretical framework in which these methodologies are inserted [A18] [B7] [B9] [B12], with also attention to generalization of experimental results [A28] and ethical issues [A32]. A comprehensive summary of the main results is reported in [B15]. In this context, competitions [B8] represent an interesting approach to the experimental analysis of whole robotic systems [C65] [C66] [B16] [A26]. From an operative point of view, [C91] presents a system that supports repeatability of some experiments on robots.

Francesco Amigoni has also substantially contributed (with leadership roles) the definition of an IEEE standard for representing the maps employed by robots for navigation [C69] [A33] [C101].

Multiagent Systems

Multiagent Decision-making, Planning, and Scheduling


Francesco Amigoni has studied multiagent decision-making, planning, scheduling, and anomaly detection in several application scenarios: ambient intelligence and energy and comfort management, space systems, water resources systems management, and electrical systems.


In ambient intelligence, several distributed devices, considered as agents, collectively operate to support the activities of the user and the needs of energy and comfort management. Distributed decision-making, planning, and scheduling are needed to implement goal-oriented behaviors. In this research topic, Francesco Amigoni has proposed a multiagent planner that can adapt its performance to the agents, and thus to the devices, currently present in the environment [A8]. The planner, called D-HTN, is based on the Hierarchical Task Network (HTN) approach. Some technological issues of the implementation of the planner in JADE are reported in [C41].


In space systems, autonomy in managing onboard activities is one of the most important requirements. In this scenario, Francesco Amigoni has developed multiagent systems to manage the activities on Earth and onboard space systems. Each device of the system is associated to an agent that is in charge of planning, scheduling, and executing its specific activities. The description of a system for onboard activities is reported in [A20]. A study on the applicability of agent technologies to space systems is reported in [C52], while a multiagent system to detect anomalies on space systems is described in [C88].


The management of water resources systems requires the accurate modeling of the entities involved and of their interactions. Distributed approaches based on multiagent systems can be fruitfully adopted to this end. A multiagent system that can help in deciding and planning the use of water resources from the perspective of a regulation body is presented in [A25], while a more evolved multi-objective approach is reported in [C71]. A multiagent system that models preference evolution in the management of water resources systems is presented in [A35].

Artificial Intelligence

A system to detect anomalies in operations of electrical circuit breakers is presented in [C95].

Two systems for classifying road accidents from textual descriptions are presented in [C102] and [C104].

Philosophical Aspects of Artificial Intelligence and Robotics

The research in this area, in cooperation with Viola Schiaffonati, addresses the study of the relationships and of the reciprocal influences between philosophy and artificial intelligence and robotics. In particular, we have addressed some foundational issues about the nature of interactions between computers and robots, on the one side, and the world [A1] and humans [A5] [C12], on the other side, and about the ethical[C4] [C33] and the design [B17] implications of these interactions. We have also addressed topics related to creativity, using the multiagent paradigm as a metaphor to represent the results of creative processes [A3] [B3] and to partially represent the creative processes themselves, proposing an operational approach to creativity [C16]. Finally, the two roles multiagent systems can play in scientific discovery, as support to scientists and as representation of results [A15] [B6], have been analyzed with respect to specific examples in [A4], [C2], and [C8]. The main results of the investigation of the roles of multiagent systems in scientific discovery are summarized in [A13].


Past Research Interests

The past research activity of Francesco Amigoni has been articulated in the following areas ([] refers to the main publications in the Publication List of the Curriculum Vitae).

Multiagent Systems

Development of Cooperative Multiagent Systems


Francesco Amigoni proposed some formalisms for describing the properties of a generic system composed of cooperative agents, both software and robotic [A2]. Such a system has been called agency to emphasize its unitary nature. Moreover, Francesco Amigoni proposed the dynamic agency architecture for developing cooperative multiagent systems. In this architecture, each agent is composed of a pair of semiagents (modules): the operative one, that offers specialized functions to operate in an environment, and the cooperative one, that integrates the agents in a uniform cooperation framework [C1]. The dynamic agency approach enables the development of a cooperative multiagent system for a given task according to a sequence of steps. Initially, the most suitable operative semiagents for the task are selected (this recruitment process has been studied theoretically in [A24] and more algorithmically in [C3] and [B1]). Then, the cooperative semiagents are installed exploiting the software technique of mobile code systems (a software framework for this purpose has been proposed in [C7]). The flexibility offered by the dynamic agency methodology in the management of agents allows their easy reuse and the dynamic variation of the system composition. In this scenario, the use of ontologies for describing the capabilities of agents is fundamental, especially in the case of robotic agents [C32] and of Internet of Things technologies [B14]. The main results obtained in this topic are collected in [A16].


Cooperative Negotiation


Francesco Amigoni has proposed the use of cooperative negotiation techniques to model complex phenomena. In particular, when a phenomenon (like some physiological processes) is described by a set of models that only partially capture the phenomenon, it is possible to associate a software agent to each partial model and let a global more complete model emerge as the agreement of the cooperative negotiation between these agents [A15] [B4]. In this research topic, a cooperative negotiation model that allows the agents to reach an agreement has been formulated and its stability studied [C21], defining the conditions that guarantee stability regardless the number of agents participating in the negotiation [A14]. This approach has been applied to the development of systems for modeling and regulating the glucose-insulin metabolism [A6] and the heart rate [A9] [A10], for modeling network normality in anomaly-based intrusion detection [C43], and for modeling operations in management of water resources systems [B13].

A bargaining protocol for obtaining Pareto optimal agreements is described in [C31].


Virtual Museums


The research activity on this topic, under the name Minerva project, was oriented to enhance the use of advanced artificial intelligence techniques to support some human activities related to the museum organization. The project involved heterogeneous contributions from different sources, both academic and not. The system that has been developed, called Minerva, automatically organizes a virtual museum starting from the works of art and the environments in which they should be displayed. Minerva is implemented as a system composed of agents that communicate and cooperate. A first version of Minerva, oriented to archeological museums, has been presented in [C6], while an updated version, oriented to archeological and design museums, has been presented in [C16]. In [A17], the main features of these versions of Minerva are summarized. Moreover, a further version of Minerva for archeological objects found on the Isola Comacina (Como, Italy) has been presented in [C35].


Supporting Research in Bio-Medical Fields


Autonomous agents can be employed to make the execution of distributed scientific experiments that span different organizations more flexible [C38]. Moreover, multiagent systems have been employed to provide simulations of biological processes, as critically analyzed in [B6].

A survey on distributed learning systems for medical applications is reported in [A43].

Autonomous Mobile Robotics

Line Segment Maps


The most used techniques for building two-dimensional maps of environments (which represent obstacles and free space) with mobile robots work incrementally by integrating a sequence of partial maps acquired by the sensors of the robots. Usually, this integration is based on the use of other localization sensors that give information on robots’ positions in the already built map. The research on this topic aims at representing obstacles with line segments and at exploiting geometrical features of partial maps to improve the quality of their integration. Francesco Amigoni has proposed important contributions in this field, including a method for integrating two partial maps [C24] and a method for integrating a sequence of partial maps [C19] without using any information about robots’ position. The obtained results are collected in [A11].

A line segment-based method for matching partial maps (or scans) that exploits geometrical features to improve the estimate provided by localization sensors is presented in [C67].

Francesco Amigoni has also proposed some methods for reducing the size of line segment-based maps by merging redundant line segments [C34] [C48] and has experimentally compared them with other methods for reducing line segments in maps [C40] [A31].


Robotic Systems for Environmental Monitoring and Search and Rescue


The research in this topic aimed at developing and implementing a system, called perceptive agency, composed of mobile robots equipped with different sensors for monitoring indoor environments. In this sense, a perceptive agency is a particular sensor network in which the nodes are implemented as perceptive mobile robots. Francesco Amigoni defined the general aspects and requirements of perceptive agencies in [A7], [C5], [C13], and [C14]. An implementation of a perceptive agency oriented to monitor electromagnetic fields, developed within the research project described in [A12], is presented in [C20] (with simulated sensors) and [C28] (with real magnetic field sensors), while [B5] summarizes the main features of this perceptive agency providing further experimental results.

The interface between human operators and autonomous robots is fundamental to obtain effective systems in search and rescue applications. A proposal relative to this topic is reported in [C61].


Flying Robot Formations


The research in this topic aimed at studying some parameters of formations of flying robots [C18].

Fragment Reassembly

The research in this topic aims at reassembling two-dimensional images starting from their fragments and without any knowledge of the final images. Francesco Amigoni has proposed a method for reassembling fragments only on the basis of their shape [C17].

Dynamic Systems Applied to Social Sciences

The goal of the research in this topic was to study a minimal model of differential equations for describing the dynamics of production in creative professions. Francesco Amigoni, in cooperation with Sergio Rinaldi, has proposed to consider, as a fundamental variable of the model, the satisfaction of a creative person, represented as the composition of internal selfmotivation and external judgment given to production [B2].


August 3, 2023