A semantically-informed multirobot system for exploration of relevant areas in search and rescue settings

Coordinated multirobot exploration involves autonomous discovering and mapping of the features of initially unknown environments by using multiple robots. Autonomously exploring mobile robots are usually driven, both in selecting locations to visit and in assigning them to robots, by knowledge of the already explored portions of the environment, often represented in a metric map. In the literature, some works addressed the use of semantic knowledge in exploration, which, embedded in a semantic map, associates spatial concepts (like ‘rooms’ and ‘corridors’) with metric entities, showing its effectiveness in improving the total area explored by robots. In this paper, we build on these results and propose a system that exploits semantic information to push robots to explore relevant areas of initially unknown environments, according to a priori information provided by human users. Discovery of relevant areas is significant in some search and rescue settings, in which human rescuers can instruct robots to search for victims in specific areas, for example in cubicles if a disaster happened in an office building during working hours. We propose to speed up the exploration of specific areas by using semantic information both to select locations to visit and to determine the number of robots to allocate to those locations. In this way, for example, more robots could be assigned to a candidate location in a corridor, so the attached rooms can be explored faster. We tested our semantic-based multirobot exploration system within a reliable robot simulator and we evaluated its performance in realistic search and rescue indoor settings with respect to state-of-the-art approaches.     

Command governor for constrained switched systems with scheduled model transition dwell times

In this paper, a switched control architecture for constrained control systems is presented. The strategy is based on command governor ideas that are here specialized to ‘optimally’ schedule switching events on the plant dynamics for improving control performance at the expense of low computational burdens. The significance of the method mainly lies in its capability to avoid constraints violation and loss of stability regardless of any configuration change occurrence in the plant/constraint structure. To this end, the concept of model transition dwell time is used within the proposed control framework to formally define the minimum time necessary to enable a switching event under guaranteed conditions on the overall stability and constraint fulfilment. Simulation results on a simple linear system and on a Cessna 182 aircraft model show the effectiveness of the proposed strategy. Copyright © 2017 John Wiley & Sons, Ltd.     

A study of co-occurrence based local features for camera model identification

Camera model identification has great relevance for many forensic applications, and is receiving growing attention in the literature. Virtually all techniques rely on the traces left in the image by the long sequence of in-camera processes which are specific of each model. They differ in the prior assumptions, if any, and in how such evidence is gathered in expressive features. In this work we study a class of blind features, based on the analysis of the image residuals of all color bands. They are extracted locally, based on co-occurrence matrices of selected neighbors, and then used to train a classifier. A number of experiments are carried out on the well-known Dresden Image Database. Besides the full-knowledge case, where all models of interest are known in advance, other scenarios with more limited knowledge and partially corrupted images are also investigated. Experimental results show these features to provide a state-of-the-art performance.     

Sensorless supervision of linear dynamical systems: The Feed-Forward Command Governor approach

This paper proposes a novel class of Command Governor (CG) strategies for input and state-related constrained discrete-time LTI systems subject to bounded disturbances in the absence of explicit state or output measurements. While in traditional CG schemes the set-point manipulation is undertaken on the basis of either the actual measure of the state or its suitable estimation, it is shown here that the CG design problem can be solved, with limited performance degradation and with similar properties, also in the case that such an explicit measure is not available. This approach, which will be referred to as the Feed-Forward CG (FF-CG) approach, may be a convenient alternative CG solution in all situations whereby the cost of measuring the state may be a severe limitation, e.g. in distributed or decentralized applications. In order to evaluate the method proposed here, numerical simulations on a physical example have been undertaken and comparisons with the standard state-based CG solution reported.     

On Nonparametric Residual Variance Estimation

In this paper, the problem of residual variance estimation is examined. The problem is analyzed in a general setting which covers non-additive heteroscedastic noise under non-iid sampling. To address the estimation problem, we suggest a method based on nearest neighbor graphs and we discuss its convergence properties under the assumption of a Hölder continuous regression function. The universality of the estimator makes it an ideal tool in problems with only little prior knowledge available.     

Logic-based automated multi-issue bilateral negotiation in peer-to-peer e-marketplaces

We present a novel logic-based framework to automate multi-issue bilateral negotiation in e-commerce settings. The approach exploits logic as communication language among agents, and optimization techniques in order to find Pareto-efficient agreements. We introduce , a propositional logic extended with concrete domains, which allows one to model relations among issues (both numerical and non-numerical ones) via logical entailment, differently from well-known approaches that describe issues as uncorrelated. Through it is possible to represent buyer’s request, seller’s supply and their respective preferences as formulas endowed with a formal semantics, e.g., “if I spend more than 30000 € for a sedan then I want more than a two-years warranty and a GPS system included”. We mix logic and utility theory in order to express preferences in a qualitative and quantitative way. We illustrate the theoretical framework, the logical language, the one-shot negotiation protocol we adopt, and show we are able to compute Pareto-efficient outcomes, using a mediator to solve an optimization problem. We prove the computational adequacy of our method by studying the complexity of the problem of finding Pareto-efficient solutions in our setting.     

On some properties of quasi-MV algebras and \sqrt{^{\prime}} quasi-MV algebras. Part II

The present paper is a sequel to Paoli F, Ledda A, Giuntini R, Freytes H (On some properties of QMV algebras and QMV algebras, submitted). We provide two representation results for quasi-MV algebras in terms of MV algebras enriched with additional structure; we investigate the lattices of subvarieties and subquasivarieties of quasi-MV algebras; we show that quasi-MV algebras, as well as cartesian and flat quasi-MV algebras, have the amalgamation property.     

Service Discovery and Negotiation With COWS

To provide formal foundations to current (web) services technologies, we put forward using COWS, a process calculus for specifying, combining and analysing services, as a uniform formalism for modelling all the relevant phases of the life cycle of service-oriented applications, such as publication, discovery, negotiation, deployment and execution. In this paper, we show that constraints and operations on them can be smoothly incorporated in COWS, and propose a disciplined way to model multisets of constraints and to manipulate them through appropriate interaction protocols. Therefore, we demonstrate that also QoS requirement specifications and SLA achievements, and the phases of dynamic service discovery and negotiation can be comfortably modelled in COWS. We illustrate our approach through a scenario for a service-based web hosting provider.     

A low-power wireless video sensor node for distributed object detection

In this paper we propose MicrelEye, a wireless video node for cooperative distributed video processing applications that involve image classification. The node is equipped with a low-cost VGA CMOS image sensor, a reconfigurable processing engine (FPGA, Microcontroller, SRAM) and a Bluetooth 100-m transceiver. It has a size of few cubic centimeters and its typical power consumption is approximately ten times less than that of typical commercial DSP-based solutions. As regards classification, a highly optimized hardware-oriented support vector machine-like (SVM-like) algorithm called ERSVM is proposed and implemented. We describe our hardware and software architecture, its performance and power characteristics. The case study considered in this paper is people detection. The obtained results suggest that the present technology allows for the design of simple intelligent video nodes capable of performing classification tasks locally.