No abstract

The limiting magnitude is a key issue for optical interferometry. Pairwise fringe trackers based on the integrated optics concepts used for example in GRAVITY seem limited to about K=10.5 with the 8m Unit Telescopes of the VLTI, and there is a general "common sense" statement that the efficiency of fringe tracking, and hence the sensitivity of optical interferometry, must decrease as the number of apertures increases, at least in the near infrared where we are still limited by detector readout noise. Here we present a Hierarchical Fringe Tracking (HFT) concept with sensitivity at least equal to this of a two apertures fringe trackers. HFT is based of the combination of the apertures in pairs, then in pairs of pairs then in pairs of groups… The key HFT module is a device that behaves like a spatial filter for two telescopes (2TSF) and transmits all or most of the flux of a cophased pair in a single mode beam. We give an example of such an achromatic 2TSF, based on very broadband dispersed fringes analyzed by grids, and show that it allows piston measures from very broadband fringes with only 3 to 5 pixels per fringe tracker. We show the results of numerical simulation indicating that our device is a good achromatic spatial filter and allowing a first evaluation of its coupling efficiency, which is similar to this of a single mode fiber on a single aperture. Our very preliminary results indicate that HFT has a good chance to be a serious candidate for the most sensitive fringe tracking with the VLTI and also interferometers with much larger number of apertures. On the VLTI the first rough estimate of the magnitude gain with regard to the GRAVITY internal FT is between 2.5 and 3.5 magnitudes in K, with a decisive impact on the VLTI science program for AGNs, Young stars and planet forming disks.

No abstract

We address the problem of autonomous underwater vehicle guidance along the boundaries of different benthic species using video information. This form of guidance provides a robust navigation behavior enabling observation of the occupancy of the sea bed independently of the presence of external position references (either GPS or installed acoustic beacons). The major innovation of the work presented concerns the image segmentation algorithm. It is an unsupervised algorithm, which identifies clusters in the space of gray level probability distributions of image neighborhoods. The metric used to compare gray level distributions is the Kullback-Leibler directed divergence, which is related to the probability of confusing members of distinct clusters. The algorithm is self-tuned, in the sense that the number of clusters is automatically determined. It works adaptively (frame-to-fiame), updating the classes' representations for each new frame, accommodating gradual lighting and texture variations within the same region. The visual controller, a simple integral law with saturation, controls heading rate to minimize the distance between the contour and the image center, while keeping a constant forward speed along the body axis. A separate controller (classic PI) keeps the robot at constant altitude from the sea bottom. The design of these controllers was based on the identified hydrodynamic model of the vehicle. The performance of the algorithm proposed is validated by real experiments conducted with the robot Phantom 500 XTL (Deep Oceans Engineering, USA)'.