In the presented scenario, an autonomous surface vehicle (ASV) equipped with a laser scanner navigates on a inland pathway surrounded and crossed by man-made structures such as bridges and locks. GPS receiver present on board experiences signal loss and multipath reflections in situation when the view of the sky is obscured by a bridge or tall buildings. In both cases, a potentially dangerous situation is provoked as the robot has no or inaccurate positioning data. A sensor data processing scheme is proposed where these gaps are smoothly filled in by positioning data generated from scan matching and registration of the laser data. This article shows preliminary results of positioning data improvement during trials in harbor-river environment.
In addition to sonar and LiDAR, vision-based ASV navigation has been explored, for example by Dunbabin al @cite_13 , Wang al @cite_10 , Heidarsson and Sukhatme @cite_8 . Despite producing information-rich data at a high frequency, standard imaging techniques do not provide the exact geometric information about the environment. Thus, with the decreasing sensor prices, LiDARs become more commonplace in autonomous vehicles, including ASVs.
@cite_10 @cite_13 @cite_8
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Over the past decade, the trajectory tracking of underactuated water surface robots (or boats, surface vessels, etc.) has been an attractive topic in the control and automation community, and numerous controllers are proposed for this challenging problem. However, most, if not all, of the existing trajectory tracking controllers of the underactuated water surface robots assume the global positions of the robots can be accurately measured. In the practical applications, the global position measurement systems are sometimes unstable or even unavailable in the working environments of the robots. To avoid the direct position measurement, this brief presents a new controller for the trajectory tracking of underactuated water surface robots using monocular visual feedback. This controller works on the basis of a novel adaptive algorithm for estimating global position of the robot online using visual feature tracking from a monocular camera, and its orientation and velocity measured by the attitude and heading reference system sensor and a velocity observer. It is proved by Lyapunov theory that the proposed adaptive visual servo controller gives rise to asymptotic tracking of a desired trajectory and convergence of the position estimation to the actual position. Experiments are conducted to validate the effectiveness and robust performance of the proposed controller. This paper describes the development of a novel vision-based autonomous surface vehicle with the purpose of performing coordinated docking manoeuvres with a target, such as an autonomous underwater vehicle, at the water's surface. The system architecture integrates two small processor units; the first performs vehicle control and implements a virtual force based docking strategy, with the second performing vision-based target segmentation and tracking. Furthermore, the architecture utilises wireless sensor network technology allowing the vehicle to be observed by, and even integrated within an ad-hoc sensor network. Simulated and experimental results are presented demonstrating the autonomous vision- based docking strategy on a proof-of-concept vehicle. We describe a technique for an Autonomous Surface Vehicle (ASV) to learn an obstacle map by classifying overhead imagery. Classification labels are supplied by a front-facing sonar, mounted under the water line on the ASV. We use aerial imagery from two online sources for each of two water bodies (a small lake and a harbor) and train classifiers using features generated from each image source separately, followed by combining their output. Data collected using a sonar mounted on the ASV were used to generate the labels in the experimental study. The results show that we are able to generate accurate obstacle maps well-suited for ASV navigation.