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In multi-object inference, the multi-object probability density captures the uncertainty in the number and the states of the objects as well as the statistical dependence between the objects. Exact computation of the multi-object density is generally intractable and tractable implementations usually require statistical independence assumptions between objects. In this paper we propose a tractable multi-object density approximation that can capture statistical dependence between objects. In particular, we derive a tractable Generalized Labeled Multi-Bernoulli (GLMB) density that matches the cardinality distribution and the first moment of the labeled multi-object distribution of interest. It is also shown that the proposed approximation minimizes the Kullback-Leibler divergence over a special tractable class of GLMB densities. Based on the proposed GLMB approximation we further demonstrate a tractable multi-object tracking algorithm for generic measurement models. Simulation results for a multi-object Track-Before-Detect example using radar measurements in low signal-to-noise ratio (SNR) scenarios verify the applicability of the proposed approach.

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M usculoskeletal disorders, the single largest category of workrelated injuries in many industrial countries, are associated with very high costs in terms of lost productivity. In highvolume production facilities, large parts of the workstation should ideally be adapted to individual workers in real time to prevent such injuries. However, in smaller production lines, especially those found in small and medium enterprises (SMEs), regularly adapting the entire workstation to accommodate flexibility is a major challenge. A solution to the problem is the development of reconfigurable human-robot collaboration (HRC) workstations and frameworks. These are key to enabling agile manufacturing, by merging the dexterity, flexibility, and ©ISTOCKPHOTO.COM/ALASHI

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The paper presents a theoretical approach to the multiagent fusion of multitarget densities based on the information-theoretic concept of Kullback-Leibler Average (KLA). In particular, it is shown how the KLA paradigm is inherently immune to double counting of data. Further, it is shown how consensus can effectively be adopted in order to perform in a scalable way the KLA fusion of multitarget densities over a peer-to-peer (i.e. without coordination center) sensor network. When the multitarget information available in each node can be expressed as a (possibly Cardinalized ) Probability Hypothesis Density (PHD), application of the proposed KLA fusion rule leads to a consensus (C)PHD filter which can be successfully exploited for distributed multitarget tracking over a peer-to-peer sensor network.

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Autonomous Underwater Vehicles (AUVs) are increasingly employed in underwater operations within many scientific and industrial tasks (e.g. Oil&Gas operations, exploration and surveillance of archaeological sites, reconnaissance and patrolling for military operations). Autonomous underwater navigation is critical due to lack of access to satellite navigation systems (e.g. the Global Positioning System, GPS) and to the typical low functioning rate of the acoustic underwater localization devices typically used. As a result, an AUV must typically proceed for long time intervals in dead-reckoning, i.e. only relying on the measurements of on-board sensors. In this context, the filtering algorithms used to estimate the state of the AUV play a fundamental role in guaranteeing satisfactory underwater navigation accuracy. In this paper, the authors present a comparison between underwater navigation systems relying on either the Extended Kalman Filter (EKF) or the Unscented Kalman Filter (UKF) for the AUV state estimation. These approaches have been currently tested offline running on the experimental data collected with the Typhoon-class AUVs (TifOne and TifTu) during different missions at sea. Typhoon is an AUV designed by the Department of Industrial Engineering of the University of Florence for exploration and surveillance of underwater archaeological sites in the framework of the THESAURUS project (2011-2013 funded by Tuscan Region) and the European ARROWS project. A performance comparison between the proposed UKF-based navigation system and the standard, EKF-based, system is here presented basing on the experimental data of different missions at sea. In particular the proposed missions are the final demo of the THESAURUS project in Livorno (Italy) in August 2013 and the preliminary tests at sea of the ARROWS project performed during the Breaking the Surface workshop in Biograd na Moru (Croatia) during October 2014. The achieved results are encouraging: the research outcome has shown the effectiveness of both navigation algorithms and the superiority of the UKF