Unmanned surface vessels (USVs) are water-borne vessels that are capable of operating on the surface of the water without any onboard human operators. USVs can operate in confined areas (ports, harbours, marinas, etc.) to conduct demanding and challenging missions such as port dredging survey, berth clearance monitoring and marine infrastructure maintenance, with significant benefits including reduced risk to personnel, improved spatial-temporal efficiency and increased operation endurance. However, when operating in confined marine environments, current USVs are usually remotely controlled. This is because in contrast to navigating in public waterways, confined marine environments are highly dynamic (the locations of docked/moored vessels in a port may be constantly changing) making static nautical charts or satellite and aerial imagery less useful for navigation. Such a factor makes the confined marine environment more highly unknown and associated with high levels of uncertainties. Autonomous exploration, as a process that can map an unknown confined environment in an automatic way, has therefore become critical to USV operation in unknown confined marine environments.
Current state-of-the-art autonomous exploration strategy employed by USVs is to leverage the Simultaneous Localisation And Mapping (SLAM) technology to build a map of an environment using sensory data without any prior information. Since SLAM is a passive process, regular teleoperation with human operators guiding the map-building process is required for existing USV platforms, making the exploration not fully autonomous. To make the SLAM based autonomous exploration an active process, planning functionality including two modules, i.e., a utility evaluation module and a path generation/selection module, has to be integrated. However, current studies about utility evaluation and path generation cannot address the issues caused by the sparse landmarks in a marine environment, which will compromise the exploration accuracy and efficiency.
This research therefore aims to develop a new active autonomous exploration framework using probabilistic inference based utility evaluation and path generation/selection. More specifically, we will construct a pseudo map which contains virtual landmarks as a proxy for an unknown confined marine environment with sparse real landmarks, and evaluate uncertainties as per marginal posterior distributions of poses and positions of virtual landmarks, respectively, using Bayesian probabilistic inference. We also propose to design a new Gaussian Process (GP) based path generation algorithm for autonomous exploration and solve the path generation problem as probabilistic inference on a factor graph. A cross-entropy optimisation method will be adapted to the path planning to enable efficient derivation of the GP mean and covariance updating rules by taking into account nonlinear constraints such as USVs' manoeuvrability.
Of key importance for the success of this work is the international collaboration with a leading marine robotics expert, Prof. Brendan Englot, Stevens Institute of Technology, to jointly develop the framework. This work will also have a close collaboration with experienced industrial partners, including Port of London Authority (PLA) and BMT Group Ltd. By working closely with PLA and BMT, innovations generated from this research will be implemented on the Otter USV to conduct use-case demonstrations (e.g., hydrographic survey) on the Tidal Thames. And the long-term vision of this international collaboration is to establish a strong UK-US research consortium on future marine innovations in advanced sensors, AI/machine learning and robotics to work collaboratively with more academic institutions, companies and regulators/organisations.
|