Traditionally, flight control laws are verified, among other analyses, by demonstrating robust stability in the flight envelope (namely, in the region of operation of aircraft). Stability requirements are given in terms of gain and phase margins. These margins give an indication of the robustness of the control laws in presence of unmodeled effects and uncertainty. This paper proposes an AI-based approach to support the stability analysis tasks by identifying a structured partition of the flight envelope, where each region of the partition exhibits locally homogeneous stability characteristics. Building on recursive partitioning methods from Machine Learning, the proposed approach leverages the interpretability of tree-based models to facilitate human expert validation and usage of the obtained results.

In this work, we concentrate on the detection of anomalous behaviors in systems operating in the physical world and for which it is usually not possible to have a complete set of all possible anomalies in advance. We present a data augmentation and retraining approach based on adversarial learning for improving anomaly detection. In particular, we first define a method for generating adversarial examples for anomaly detectors based on Hidden Markov Models (HMMs). Then, we present a data augmentation and retraining technique that uses these adversarial examples to improve anomaly detection performance. Finally, we evaluate our adversarial data augmentation and retraining approach on four datasets showing that it achieves a statistically significant performance improvement and enhances the robustness to adversarial attacks. Key differences from the state-of-the-art on adversarial data augmentation are the focus on multivariate time series (as opposed to images), the context of one-class classification (in contrast to standard multi-class classification), and the use of HMMs (in contrast to neural networks).

In several applications, autonomous mobile robots benefit from knowing the structure of the indoor environments where they operate. This knowledge can be extracted from the metric maps built (e.g., using SLAM algorithms) from the data perceived by the robots' sensors. The layout is a way to represent the structure of an indoor environment with geometrical primitives. Most of the current methods for reconstructing the layout from a metric map represent the parts of the environment that have been fully observed. In this paper, we propose an approach that predicts the layout of rooms which are only partially known in a 2D metric grid map. The prediction is made according to the global structure of the environment, as identified from its known parts. Experiments show that our approach is able to effectively predict the layout of several indoor environments that have been observed to different degrees.

In the Multiagent Connected Path Planning problem (MCPP), a team of agents moving in a graph-represented environment must plan a set of start-goal joint paths which ensures global connectivity at each time step, under some communication model. The decision version of this problem asking for the existence of a plan that can be executed in at most a given number of steps is claimed to be NP-complete in the literature. The NP membership proof, however, is not detailed. In this paper, we show that, in fact, even deciding whether a feasible plan exists is a PSPACE-complete problem. Furthermore, we present three algorithms adopting different search paradigms, and we empirically show that they may efficiently obtain a feasible plan, if any exists, in different settings.

The development of good experimental methodologies for robotics takes often inspiration from general principles of experimental practice. Repeatability prescribes that experiments should involve several trials in order to guarantee that results are not achieved by chance, but are systematic, and statistically significant trends can be identified. In this paper, we propose an approach to improve the repeatability of experiments performed in robotics. In particular, we focus on the domain of SLAM (Simultaneous Localization And Mapping) and we introduce a system that exploits simulations to generate a large number of test data on which SLAM algorithms are automatically evaluated in order to obtain consistent results, according to the principle of repeatability.

In several multirobot applications in which communication is limited, the mission could require the robots to iteratively take coordinated joint decisions on how to spread out in the environment and on how to reconnect with each other to share data and compute plans. Exploration and surveillance are examples of these applications. In this paper, we consider the problem of computing robots' paths on a graph-represented environment for restoring connections at minimum traveling cost. We call it the multirobot reconnection problem, we show its NP-hardness and hardness of approximation on some important classes of graphs, and we provide optimal and heuristic algorithms to solve it in practical settings. The techniques we propose are then exploited to derive a new efficient planning algorithm for a relevant connectivity-constrained multirobot planning problem addressed in the literature, the multirobot informative path planning with periodic connectivity problem.

The importance of communication in many multirobot information-gathering tasks requires the availability of reliable communication maps. These provide estimates of the radio signal strength and can be used to predict the presence of communication links between different locations of the environment. In the problem we consider, a team of mobile robots has to build such maps autonomously in a robot-to-robot communication setting. The solution we propose models the signal's distribution with a Gaussian Process and exploits different online sensing strategies to coordinate and guide the robots during their data acquisition. Our methods show interesting operative insights both in simulations and on real TurtleBot 2 platforms.

Semantic mapping for autonomous mobile robots includes the place classification task that associates semantic labels (like `corridor' or `office') to rooms perceived in indoor environments. The mainstream approaches to place classification are characterized by local reasoning, where only features relative to the neighbourhood of each room are considered. In this paper, we propose a method for global reasoning on the whole structure of buildings, considered as single structured objects. We use a statistical relational learning algorithm, called kLog, and we compare it against a classifier, Extra-Trees, which resembles classical local approaches, in three tasks: classification of rooms, classification of entire floors of buildings, and validation of simulated worlds. Our results show that our global approach performs better than local approaches when the classification task involves reasoning on the regularities of buildings and when available information about rooms is coarse-grained.

In multirobot exploration under centralized control, communication plays an important role in constraining the team exploration strategy. Recurrent connectivity is a way to define communication constraints for which robots must connect to a base station only when making new observations. This paper studies effective multirobot exploration strategies under recurrent connectivity by considering a centralized and asynchronous planning framework. We formalize the problem of selecting the optimal set of locations robots should reach, provide an exact formulation to solve it, and devise an approximation algorithm to obtain efficient solutions with a bounded loss of optimality. Experiments in simulation and on real robots evaluate our approach in a number of settings.

We consider the problem of computing patrolling strategies under communication constraints for a team of autonomous robots employed in repeated surveillance missions on a set of predefined locations. We assume the presence of a communication infrastructure providing only some regions of the environment with a communication link to a mission control center (MCC). We define the problem of computing a joint patrolling strategy that minimizes communication latencies, defined as the delays between inspecting some locations and reporting the outcome to the MCC. We provide and experimentally evaluate a MILP formulation and a heuristic method.

Consider a mobile robot exploring an initially unknown school building and assume that it has already discovered some classrooms, offices, and bathrooms. What can the robot infer about the presence and the locations of other classrooms and offices in the school building? This paper makes a step toward providing an answer to the above question by proposing a system based on a generative model that is able to represent the topological structures and the semantic labeling schemas of buildings and to predict the structure and the schema for unexplored portions of these environments. We represent the buildings as undirected graphs, whose nodes are rooms and edges are physical connections between them. Given an initial knowledge base of graphs, our approach, relying on a spectral analysis of these graphs, segments each graph for finding significant subgraphs and clusters them according to their similarity. A graph representing a new building or an unvisited part of a building is eventually generated by sampling subgraphs from clusters and connecting them.

Over the years, proposals have been made to employ line segments to build 2D maps of indoor environments. One of the basic steps of these approaches is the matching between scans (or, more generally, sets) of line segments, which is usually addressed using variants of the Iterative Closest Line (ICL) paradigm. ICL is based on the idea of associating closest line segments belonging to the two scans and of reducing the distance between them. In this paper, we propose two algorithms that go beyond this approach by exploiting the mutual compatibility between associations of line segments. Experimental results show that our algorithms significantly outperform, in terms of matching accuracy, traditional algorithms based on ICL, at the cost of a slightly longer execution time.

Multiagent systems constitute an independent topic at the intersection between distributed computing and artificial intelligence. As the algorithmic techniques and the applications for multiagent systems have been continuously developing over the last two decades reaching significantly mature stages, many methodological problems have been addressed. In this paper, we aim to contribute to this methodological assessment of multiagent systems by considering the problem of choosing, or recruiting, a subset of agents from a set of available agents to satisfy a given request. This problem, which we call problem of recruitment, is encountered, for example, in matchmaking and in task allocation. We present and study a novel formal approach to the problem of recruitment, based on the algebraic formalism of lattices. The resulting formal framework can support the development of algorithms for automatic recruitment.

Robotic exploration is an on-line problem in which autonomous mobile robots incrementally discover and map the physical structure of initially unknown environments. Usually, the performance of exploration strategies used to decide where to go next is not compared against the optimal performance obtainable in the test environments, because the latter is generally unknown. In this paper, we present a method to calculate an approximation of the optimal (shortest) exploration path in an arbitrary environment. We consider a mobile robot with limited visibility, discretize a two-dimensional environment with a regular grid, and formulate a search problem for finding the optimal exploration path in the grid, which is solved using A*. Experimental results show the viability of our approach for realistically large environments and its potential for better assessing the performance of on-line exploration strategies.

Pursuit evasion problems, in which evading targets must be cleared from an environment, are encountered in surveillance and search and rescue applications. Several works have addressed variants of this problem in order to study strategies for the pursuers. As a common trait, many of these works present results in the general form: given some assumptions on the environment, on the pursuers, and on the evaders, upper and lower bounds are calculated for the time needed for (the probability of, the resources needed for, ...) clearing the environment. The question “what is the optimal strategy for a given pursuer in a given environment to clear a given evader?” is left largely open. In this paper, we propose a game theoretical framework that contributes in finding an answer to the above question in a version of the pursuit evasion problem in which the evader enters and exits a grid environment and the pursuer has to intercept it along its path. We adopt a criterion for optimality related to the probability of capture. We experimentally evaluate the proposed approach in simulated settings and we provide some hints to generalize the framework to other versions of the pursuit evasion problem.

Autonomous mobile robots are a promising technology for search and rescue scenarios, where an initially unknown environment has to be explored to locate human victims. Robots can exploit exploration strategies to autonomously move around the environment. Most of the strategies proposed in literature are based on the idea of evaluating a number of candidate locations according to ad hoc utility functions that combine different criteria. In this paper, we show some of the advantages of using a more theoretically-grounded approach, based on Multi-Criteria Decision Making (MCDM), to define exploration strategies for robots employed in search and rescue applications. We implemented our MCDM-based exploration strategies within an existing robot controller and we evaluated their performance in a simulated environment.

Game theoretical approaches have been recently used to develop patrolling strategies for mobile robots. The idea is that the patroller and the intruder play a game, whose outcome depends on the combination of their actions. From the analysis of this game, an optimal strategy for the patrolling robot can be derived. Although game theoretical approaches are promising, their applicability in real settings is still an open problem. In this paper, we experimentally evaluate the practical applicability of the most general game theoretical approach for patrolling strategies, called BGA model. Experiments are conducted by using USARSim, with the goal of studying the behavior of the optimal patrolling strategy returned by the BGA model both in situations that violate its idealized assumptions and in comparison with other patrolling strategies that can be developed with much less computational effort.

The fundamental role of autonomous agents in managing activities of space systems has emerged some years ago with the NASA's Remote Agent Experiment. However, the possible advantages of employing multiple agents to manage activities on a single space

Using autonomous mobile robots to patrol environments for detecting intruders is a topic of increasing relevance for its possible applications. A large part of strategies for mobile patrolling robots proposed so far adopt some kind of random movements. Although these strategies are unpredictable for an intruder, they are not always efficient in getting the patroller a large expected utility. In this paper we propose an approach that considers a model of the adversary in a game theoretic framework to find optimally-efficient patrolling strategies. We show that our approach extends those proposed in literature and we experimentally analyze some of its features.

The ability of building maps of environments where they operate is one of the main requirements for autonomous mobile robots. An efficient map building process is based on a good exploration strategy that determines the most convenient observation positions in a partially known environment in order to incrementally map it. Several exploration strategies have been proposed in literature but their comparative evaluations are rare. In this paper, we experimentally compare some exploration strategies in order to contribute to assess their strengths and weaknesses.

Segment-based maps are emerging as an efficient way to represent the environments in which mobile robots operate. When compared to grid-based maps, maps composed of line segments usually need less space to be stored. However, very little effort has been devoted to methods that allow to reduce the size of segment-based maps by removing redundant line segments that represent the same object in the environment. This problem is usually addressed with ratheradhocmethods that are embedded in mapping systems. In this paper, we put forward the problem of reducing the size of segment-based maps by presenting a survey of the existing methods and by experimentally evaluating some of them. Our results can be used to set out some guidelines for the development of a general approach to reducing redundant line segments in maps.

A way to significantly advance robotic science is to perform experiments that can be replicated by other researchers and be used to compare different methods. This happens rarely in current robotics research. In this paper we present a methodology for performing experimental activities in the area of robotic mapping. The proposed methodology prescribes a number of issues that should be addressed when experimentally validating a mapping method. We present the application of the proposed methodology to a mapping system we have developed.

Most map building methods employed by mobile robots are based on the assumption that an estimate of robot poses can be obtained from odometry readings or from observing landmarks or other robots. In this paper we propose methods to build a global geometric map by integrating scans collected by laser range scanners without using any knowledge about the robots' poses. We consider scans that are collections of line segments. Our approach increases the flexibility in data collection, since robots do not need to see each other during mapping, and data can be collected by multiple robots or a single robot in one or multiple sessions. Experimental results show the effectiveness of our approach in different types of indoor environments.

Simulating and controlling physiological phenomena are notoriously complex tasks to tackle and require accurate models of the phenomena of interest. Currently, most physiological processes are described by a set of partial models capturing specific aspects of the phenomena, and usually their composition does not produce effective comprehensive models. A current open issue is thus the development of techniques able to effectively describe a phenomenon starting from partial models. This is particularly relevant for heart rate regulation modeling where a large number of heterogeneous partial models exists. In this paper we make the original proposal of adopting a multiagent paradigm, called anthropic agency, to provide a powerful and flexible tool for combining partial models of heart rate regulation for adaptive cardiac pacing applications. The partial models are embedded in autonomous computational entities, called agents, that cooperatively negotiate in order to smooth their conflicts on the values of the variables forming the global model the multiagent system provides. We experimentally evaluate our approach and we analyze its properties.

Exploration strategies are used to guide mobile robots in building maps of environments. Usually, exploration strategies work greedily by evaluating a number of candidate observation positions on the basis of a utility function and selecting the best one. The utility functions are defined in an ad hoc manner as the compositionof values measuring different features of a candidate observation position, such as the travelling cost and the estimated information gain. In this paper, we propose a more general way to define an exploration strategy through multi-objective optimization. In our approach, the values of the features are kept separated without combining them in a particular utility function. Experimental results demonstrate the effectiveness of our method.

The development of ambient intelligence (AmI) applications that effectively adapt to the needs of the users and environments requires, among other things, the presence of planning mechanisms for goal-oriented behavior. Planning is intended as the ability of an AmI system to build a course of actions that, when carried out by the devices in the environment, achieve a given goal. The problem of planning in AmI has not yet been adequately explored in literature. We propose a planning system for AmI applications, based on the hierarchical task network (HTN) approach and called distributed hierarchical task network (D- HTN), able to find courses of actions to address given goals. The plans produced by D-HTN are flexibly tailored to exploit the capabilities of the devices currently available in the environment in the best way. We discuss both the architecture and the implementation of D-HTN. Moreover, we present some of the experimental results that validated the proposed planner in a realistic application scenario in which an AmI system monitors and answers the needs of a diabetic patient.

The map building methods usually employed by mobile robots are based on the assumption that an estimate of the position of the robot can be obtained from odometry readings. In this paper we propose three methods that build a geometrical global map by integrating partial maps without using any odometry information. We focus on the problem of integrating a sequence of partial maps that specifies the order in which the partial maps must be integrated. Experimental results show the effectiveness of our approach in different types of environments.

The reassembly of fragments to reconstruct images and objects is a problem often encountered in several applications, ranging from archeology to medicine. In this paper we present a novel method for reassembling two 2D fragments. The proposed method is based on the information extracted from the outlines and from the color contents of the fragments, without relying on any knowledge of the final image. The techniques illustrated in this paper constitute the core of a more general method for reassembling n fragments we have developed. However, here we concentrate mainly on the description of the algorithms for matching two fragments and on their experimental analysis and validation.

In the field of distributed artificial intelligence, the cooperation among intelligent agents is a matter of growing importance. We propose a new machine, called agency, which is devoted to solve complex problems by means of cooperation among agents, where each agent is able to perform inferential activities. The aim of this paper is to give rigorous and formal descriptions of agency and, using the descriptions, to define and prove some interesting properties. The descriptions are based on three formalisms: multilanguage systems, directed hypergraphs, ER Petri nets. The work is a step in the direction of building a methodology for the project and the development of systems operating in real-world applications. We give a theoretical background on which new techniques can be implemented for testing the requirements of systems of distributed artificial intelligence such as agencies. The fundamental formalism in describing agencies is multilanguage system; starting from it we capture some particular issues (i.e., structure and evolution of an agency) by means of hypergraphs and ER Petri nets. The formalisms support the definition and proof of properties (such as fairness of cooperation among agents). ©1999 John Wiley & Sons, Inc.