Maria-João Rendas

Hybrid navigation system for long range operation

Describes a navigation system that allows long range navigation by combining two distinct modes: local positioning with respect to a long baseline array of known localization (reference points) and autonomous mode in-between reference points, relying only on sonar/Doppler and depth information. The areas of transponder-based navigation are defined by the maximum range of transponder operation. In both modes, a Kalman based design approach was chosen, using, when necessary, the available position estimates to characterize the errors associated with the filter inputs. The authors present simulation results that illustrate the systems behavior for a typical maneuvering operation.

Exploiting natural contours for automatic sonar-to-video calibration

The paper addresses the problem of coregistration of data acquired by two distinct ego-centric exteroceptive sensors mounted in an autonomous underwater vehicle: a video camera and a mechanically scanning profiler sonar. As it is the case for the http://www.laas.fr/reforme-CNRS.html experimental platform used in this study1, these two perception sensors can often be installed in flexible geometric configurations on the robots crash frame, and thus their relative orientation and displacement may change from mission to mission, even if during each mission they are kept fixed. The algorithm exploits the presence in the sea bottom of a boundary between two regions that can be discriminated both in the video frames and on the sonar profiles, and requires no specially tuned artificial settings. The registration is solved in a completely autonomous manner, using the ability of the robot to track the natural contour based on the video data. Our algorithm finds the mapping from the sonar coordinates to the image coordinates that minimizes the average entropy of a probabilitic model that is fit to the inverse mapping of the segmented images. It does not require reconstruction of the robots trajectory during data acquisition, and is thus insensitive to positioning problems.

Identification and control of the Phantom 500 body motion

Addresses the identification and the control of the vertical motion of the ROV Phantom. The problems are split in two decoupled problems.: the propeller motion and the diving motion. A maximum likelihood based identification method is used for each dynamic model. The design of the inner and outer controllers is based on the classical root locus method. Numerical experiments demonstrate that the designed controllers yield satisfactory results in terms of stability, tracking performance and robustness.

A robust visual attention system for detecting manufactured objects in underwater video

The paper presents an approach to detect objects in underwater images. The method is composed of two main steps. The first step detects the motion of contrasting neighboring regions in consecutive frames, based on the Minimum Description Length test. The second step adapts a statistical snake to the boundary between the adjacent regions. The method is evaluated on real images and compared to classical snakes

Statistical environment representation for navigation in natural environments

Abstract In this paper we present a novel approach to mobile robot navigation based on environment representation by statistical models. Natural unstructured scenes are interpreted as realizations of Random Closed Sets (RCSs), whose macroscopic characteristics are mapped. Contrary to feature based approaches, this environment representation does not require the existence of remarkable objects in the workspace, and is robust with respect to small dynamic changes. We address the relocalization problem assuming that a statistical model, serving as a map of the environment, is available a priori. Simulation results demonstrate the feasibility of our approach.

Resolving narrowband coherent paths with non-uniform arrays

An algorithm is presented for estimating the directions of arrival (DOAs) of multiple narrowband (NB) sources (possible completely correlated) from observations by an array of arbitrary geometry. As a preprocessor, the algorithm extends the application of NB high-resolution direction-finding schemes beyond the linear uniform array configuration, which these methods usually assume when handling coherent sources. The novelty of the approach is that both geometrical considerations and results from optimal estimation theory are used.<<ETX>>

Learning safe navigation in uncertain environments

Abstract We present a strategy for the navigation of mobile robots in open sparse uncertain environments. Our work assumes that the robot has no access to external informations about its absolute position, and that the environment consists of a set of objects whose separation is large compared to the range of the perception sensors. The approach is based on: (i) maximizing the probability of successful transition between any pair of known objects — by requiring if necessary that the robot uses other objects as intermediate landmarks — and (ii) guaranteeing — by controlling the extension of dead-reckoning motions and resorting if necessary to active search for perceptual information — that a reliable path to the homing region always exists. The paper presents experimental results that clearly demonstrate the increase in autonomy resulting from the proposed navigation strategy.

Using a priori current knowledge on AUV navigation

This paper addresses the problem of incorporating, in the navigation system of AUVs, existing a priori information concerning the current fields in the site of underwater robotic missions. In our approach, deterministic information, in the form of expected current field in the mission area, are used to spatially control the gain of the Kalman filter that estimates the sea current. The robustness of this approach and its extension, taking into account the uncertainty affecting the a priori information, is also discussed. Several mission simulations using the predicted current field near the Rhone river estuary are presented, demonstrating the adequacy of the methodology proposed.

Shape recognition: a fuzzy approach

We address the problem of shape recognition in the context of autonomous navigation of mobile robots. We present an association procedure which is able to establish the correspondence between elements of a learned fuzzy internal representation of the environment and currently perceived objects. Using this procedure, the robot is able to periodically decrease the uncertainty with respect to its position. In this way, the information provided by its perception sensors results in an effective extension of the robots autonomy.

Cramer-Rao bounds for passive range and depth in a vertically inhomogeneous medium

Cramer-Rao bounds are established for passive location in a multipath environment with an array of multiple sensors. The source signature is wideband stationary and the background noise is white. General expressions are derived for the bounds, and the performance gain contributed by the interpath delays over location techniques based on wavefront curvature only (i.e. spatial processing across the array of sensors) is shown.<<ETX>>