Decentralized Connectivity-Preserving Deployment of Large-Scale Robot Swarms

We present a decentralized and scalable approach for deployment of a robot swarm. Our approach tackles scenarios in which the swarm must reach multiple spatially distributed targets, and enforce the constraint that the robot network cannot be split. The basic idea behind our work is to construct a logical tree topology over the physical network formed by the robots. The logical tree acts as a backbone used by robots to enforce connectivity constraints. We study and compare two algorithms to form the logical tree: outwards and inwards. These algorithms differ in the order in which the robots join the tree: the outwards algorithm starts at the tree root and grows towards the targets, while the inwards algorithm proceeds in the opposite manner. Both algorithms perform periodic reconfiguration, to prevent suboptimal topologies from halting the growth of the tree. Our contributions are (i) The formulation of the two algorithms; (ii) A comparison of the algorithms in extensive physics-based simulations; (iii) A validation of our findings through real-robot experiments.Comment: 8 pages, 8 figures, submitted to IROS 201

Deep Water Archaeology in Italy and in the Tyrrhenian Sea

The paper presents the history of the research in Deep water archaeology in Italy and the recent activities carried out by Fondazione Azionemare in collaboration with the Dipartimento di Studi Umanistici of the Università Ca’ Foscari Venezia in the Tyrrhenian Sea. Through a preliminary presentation of some shipwrecks dated to the Roman period, discovered by the Fondazione Azionemare and investigated, thanks also the photogrammetry, with the archaeologists of the Venetian university, the article analysis the characteristics and the potentialities of these contexts which present an excellent level of conservation

A Byzantine Shipwreck from Cape Stoba, Mljet, Croatia: An interim report

The Cape Stoba shipwreck is located on the seabed off the island of Mljet in Croatia at a depth of 21-28 m. Following initial investigation in 1975, four seasons of excavation have been carried between 2010 and 2014 by the Department for Underwater Archaeology of the Croatian Conservation Institute, joined by the Department of Studi Umanistici of the Universita Ca' Foscari of Venice from 2012. The wreck-site is evidenced by a cargo of nine amphora types dated to the 10th-11th century AD, produced in the Eastern Mediterranean and Black Sea area, and glassware of Levantine production. The only direct evidence of the ship itself to date, is one iron anchor.Apstraktna Brodolom kod rta Stoba nalazi se u podmorju otoka Mljeta u Hrvatskoj na dubini od 21-28 metara. Nakon pronalaska brodoloma i prvih istraivanja u 1975. godini, provedene su etiri kampanje istraivanja u razdoblju od 2010. do 2014. godine od strane Odjela za podvodnu arheologiju Hrvatskog restauratorskog zavoda. Od 2012. godine u istraivanjima se pridruuju djelatniciDipartimento di Studi Umanistici, Sveuilita 'Ca' Foscari u Veneciji. Brodolom sadri ostatke trgovakog tereta s najmanje devet tipova amfora datiranih u 10./11. stoljee, proizvedenih na podruju istonog Mediterana i Crnoga mora te stakleno posude levantske proizvodnje. Pronadeno eljezno sidro do sada je jedini nalaz izravno vezan uz brod.Astratto Il relitto di Cape Stoba e localizzato sui fondali dell'isola di Mljet in Croazia ad una profondita di 21-28 m. In seguito ad una prima indagine, eseguita nel 1975, tra il 2010 e il 2014 il Dipartimento di Archeologia Subacquea del Croatian Conservation Institute, dal 2012, in collaborazione con il Dipartimento di Studi Umanistici dell'Universita Ca' Foscari di Venezia, ha condotto quattro campagne di scavo. Il relitto presenta un carico di nove tipi di anfore datate al 10-11 degrees secolo d.C., prodotte nel Mediterraneo Orientale e nell'area del Mar Nero, e vasi in vetro di produzione levantina. L'unica evidenza diretta della nave al momento e un'ancora in ferro

Multiframe visual-inertial blur estimation and removal for unmodified smartphones

Pictures and videos taken with smartphone cameras often suffer from motion blur due to handshake during the exposure time. Recovering a sharp frame from a blurry one is an ill-posed problem but in smartphone applications additional cues can aid the solution. We propose a blur removal algorithm that exploits information from subsequent camera frames and the built-in inertial sensors of an unmodified smartphone. We extend the fast non-blind uniform blur removal algorithm of Krishnan and Fergus to non-uniform blur and to multiple input frames. We estimate piecewise uniform blur kernels from the gyroscope measurements of the smartphone and we adaptively steer our multiframe deconvolution framework towards the sharpest input patches. We show in qualitative experiments that our algorithm can remove synthetic and real blur from individual frames of a degraded image sequence within a few seconds