Among autonomous surface vehicles, sailing robotics could be a promising technology for long-term missions and semipersistent presence in the oceans. Autonomy of such vehicles in terms of energy will be achieved by renewable solar and wind power sources. Autonomy in terms of sailing decision will be achieved by innovative perception and navigation modules. The main contribution of this paper is to propose a complete hardware and software architecture for an autonomous sailing robot. The hardware architecture includes a comprehensive set of sensors and actuators as well as a solar panel and a wind turbine. For obstacle detection, a segmentation is performed on data coming from an omnidirectional camera coupled with an inertial measurement unit and a sonar. For navigation and control of the vehicle, a potential-based reactive path-planning approach is proposed. The specific sailboat kinematic constraints are turned into virtual obstacles to compute a feasible and optimal heading in terms of cost of gybe and tack maneuver as well as safety relative to obstacle danger. Finally, field test experiments are presented to validate the various components of the system.

Toward an Autonomous Sailing Boat

Automatic sea-surface object detection and tracking for safe autonomous underwater vehicle and submarine surfacing is a critical issue in relation to the accidents reported in the last decades. Here, we propose an efficient tool to detect and track sea-surface obstacles by processing forward-looking sonar images. The proposed method can detect either still or moving objects with and without wake. For each image sequence, a sequential procedure is proposed to detect various obstacle signatures. Then, target positions and velocities are estimated in Cartesian coordinates using the debiased converted measurement Kalman filter and the joint probabilistic data association filter. Detection and tracking stages exchange information in order to reduce the number of false alarms. Promising results are obtained using real data collected at sea with various objects and scenarios.

Automatic Sea-Surface Obstacle Detection and Tracking in Forward-Looking Sonar Image Sequences

A robust obstacle detection and avoidance system is essential for long term autonomy of autonomous underwater vehicles (AUVs). Forward looking sonars are usually used to detect and localize obstacles. However, high amounts of background noise and clutter present in underwater environments makes it difficult to detect obstacles reliably. Moreover, lack of GPS signals in underwater environments leads to poor localization of the AUV. This translates to uncertainty in the position of the obstacle relative to a global frame of reference. We propose an obstacle detection and avoidance algorithm for AUVs which differs from existing techniques in two aspects. First, we use a local occupancy grid that is attached to the body frame of the AUV, and not to the global frame in order to localize the obstacle accurately with respect to the AUV alone. Second, our technique adopts a probabilistic framework which makes use of probabilities of detection and false alarm to deal with the high amounts of noise and clutter present in the sonar data. This local probabilistic occupancy grid is used to extract potential obstacles which are then sent to the command and control (C2) system of the AUV. The C2 system checks for possible collision and carries out an evasive maneuver accordingly. Experiments are carried out to show the viability of the proposed algorithm.

Robust underwater obstacle detection and collision avoidance

Navigation is a crucial issue for the operation of autonomous underwater vehicle (AUV). Range sensors mounted on an AUV can provide real-time information about the surroundings. The use of an active sonar sensor in an enclosed shallow water environment results in received echoes that may reflect several times before attenuation. Furthermore, echo intensities from boundaries are much higher than those from targets, which impedes their detection. This paper presents an innovative method for the detection of underwater objects (in this case, spherical shapes) in such environments for AUV navigation. A mechanically scanning sonar sensor was used in laboratory based trials. Experiments have been conducted in a cylindrical water-tank with an elliptical cross section. Results indicate that the presented method leads efficient and stable detection of both quiescent and moving objects. Potential applications include the detection of targets inside shipwrecks and/or hostile environments, e.g. discovering suspicious objects in the shallow water near harbor areas or water channels connected to the sea.

Automatic object detection for AUV navigation using imaging sonar within confined environments

Presents corrections to the article, "Automatic sea-surface obstacle detection and tracking in forward-looking sonar image sequences,” (Karoui, I., et al.) EEE Trans. Geosci. Remote Sens., vol. 53, no. 8, pp. 4661–4669, Aug. 2015.

Corrections to “Automatic Sea-Surface Obstacle Detection and Tracking in Forward-Looking Sonar Image Sequences”

Autonomous underwater vehicles (AUV) are expected to perform survey missions in both known and unknown environments. While the primary mission of an AUV is data collection, generally achieved with a sidescan sonar or a multibeam echosounder, another key task is to guaranty its own security. This paper deals with the problem of obstacle detection and avoidance by means of a forward looking sonar (FLS) mounted on the GESMA Redermor experimental AUV.

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A. Hetet

Papers

AUV (Redermor) Obstacle Detection and Avoidance Experimental Evaluation