Mission

Over the last decade the research in “field robotics” has resulted in development of small-sized (~2m long) unmanned surface vehicles (USVs) that can be manually guided or used to follow a pre-programmed path. Due to their portability and ability to navigate relatively shallow waters and narrow marinas, their potential use is indeed large, ranging from coastal water and environmental surveillance, to inspection of man-made structures above and below water surface.

A lot of research in USV has been dedicated to development of hardware, low-level guidance, control, self-organization and communication systems, but the level of autonomy in small-sized USVs is still relatively low. The reason is that research in advanced environment perception capabilities required for a long-term autonomous performance in uncontrolled environments lags behind the control and hardware research. Cameras as light-weight, low-power, information-rich sensors are becoming a viable alternative or addition to other sensorial modalities.

The project overarching goal is to develop functionalities required for robust autonomous navigation of USVs in uncontrolled environments, primarily relying on the captured visual information. The objectives are to develop efficient and robust computer vision approaches for obstacle detection, long-term tracking and fusion with other sensors and camera modalities. A critical requirement of the approaches will be real-time performance, environment adaptation and long-term robustness to temporary failures of sensory information and visual uncertainties. We will propose a framework that will combine such approaches into a model of robot environment, thus enabling robust long-term fully autonomous operation. The developed framework will be verified and validated on an existing integrated system, a USV, performing in real environment.

The work is divided into six work packages:

Development of robust obstacle detection approaches able to detect and extract 3D position of large as well as small obstacles (WP1).
Development of robust tracking approaches tailored to USV dynamics that enable target re-detection and re-identification (WP2).
Development of agile USV environment model that builds a map of USV surrounding, fuses results of multiple sensors, detection and tracking results into a common representation (WP3).
Construction of challenging datasets for objective offline validation of the developed methods and tests of selected methods onboard USV (WP4).
Work packages WP5 and WP6 contain support activities such as results dissemination and project management. In the following we detail the work packages and tasks.

Project phases: