Filtering based multi-sensor data fusion algorithm for a reliable unmanned surface vehicle navigation

When considering the working conditions under which an unmanned surface vehicle (USV) operates, the navigational sensors, which already have inherent uncertainties, are subjected to environment influences that can affect the accuracy, security and reliability of USV navigation. To combat this, multi-sensor data fusion algorithms will be developed in this paper to deal with the raw sensor measurements from three kinds of commonly used sensors and calculate improved navigational data for USV operation in a practical environment. Unscented Kalman Filter, as an advanced filtering technology dedicated to dealing with non-linear systems, has been adopted as the underlying algorithm with the performance validated within various computer-based simulations where practical, dynamic navigational influences, such as ocean currents, provide force against the vessel’s structure, are to be considered

Robust Multi-sensor Data Fusion for Practical Unmanned Surface Vehicles (USVs) Navigation

The development of practical Unmanned Surface Vehicles (USVs) are attracting increasing attention driven by their assorted military and commercial application potential. However, addressing the uncertainties presented in practical navigational sensor measurements of an USV in maritime environment remain the main challenge of the development. This research aims to develop a multi-sensor data fusion system to autonomously provide an USV reliable navigational information on its own positions and headings as well as to detect dynamic target ships in the surrounding environment in a holistic fashion. A multi-sensor data fusion algorithm based on Unscented Kalman Filter (UKF) has been developed to generate more accurate estimations of USV’s navigational data considering practical environmental disturbances. A novel covariance matching adaptive estimation algorithm has been proposed to deal with the issues caused by unknown and varying sensor noise in practice to improve system robustness. Certain measures have been designed to determine the system reliability numerically, to recover USV trajectory during short term sensor signal loss, and to autonomously detect and discard permanently malfunctioned sensors, and thereby enabling potential sensor faults tolerance. The performance of the algorithms have been assessed by carrying out theoretical simulations as well as using experimental data collected from a real-world USV projected collaborated with Plymouth University. To increase the degree of autonomy of USVs in perceiving surrounding environments, target detection and prediction algorithms using an Automatic Identification System (AIS) in conjunction with a marine radar have been proposed to provide full detections of multiple dynamic targets in a wider coverage range, remedying the narrow detection range and sensor uncertainties of the AIS. The detection algorithms have been validated in simulations using practical environments with water current effects. The performance of developed multi-senor data fusion system in providing reliable navigational data and perceiving surrounding environment for USV navigation have been comprehensively demonstrated

Dynamic positioning of floating caissons based on the UKF filter under external perturbances induced by waves

This paper presents a dynamic positioning control scheme for concrete caissons in an attempt to automate part of the manoeuvres which usually require a complex deploy of personnel and equipment for port infrastructures development. The aim of this paper is to propose a control scheme, which is able to provide a reduction in costs and an improvement in security for the dynamic positioning manoeuvres . To do so, a dual loop controller is developed and the unscented Kalman filter is applied for states and perturbances estimation. Furthermore, a control allocation algorithm is proposed based on anchoring lines and winches. Finally, some simulations are performed to verify the effectiveness of the proposed approach.The Spanish FEDER/Ministry of Science, Innovation and Universities — State Research Agency (Fig. 14) is greatly acknowledged for funding our research through SAFE Project (Desarrollo de un Sistema Autónomo para el Fondeo de Estructuras para Obras Marítimas), Grant Agreement: RTC-2017-6603-4. The authors would like to thank FCC CO as a collaborator in the development of the SAFE Project. R. Guanche also acknowledges financial support from the Ramon y Cajal Program (RYC-2017-23260) of the Spanish Ministry of Science, Innovation and Universities

An Underwater Vehicle Navigation System Using Acoustic and Inertial Sensors

Unmanned Underwater Vehicles (UUVs) have become an essential tool for different underwater tasks. Compared with other unmanned systems, the navigation and localization for UUVs are particularly challenging due to the unavailability of Global Positioning System (GPS) signals underwater and the complexity of the unstable environment. Alternative methods such as acoustic positioning systems, Inertial Navigation Systems (INS), and the geophysical navigation approach are used for UUV navigation. Acoustic positioning systems utilize the characteristics of acoustic signals that have a lower absorption rate and a more extended propagation distance than electromagnetic signals underwater. The significant disadvantage of the INS is the “drift,” the unbounded error growth over time in the outputs. This thesis is aimed to study and test a combined UUV navigation system that fuses measurements from the INS, Doppler Velocity Log (DVL), and Short Baseline (SBL) acoustic positioning system to reduce the drift. Two Kalman filters are used to do the fusion: the Extended Kalman Filter (EKF) and the Unscented Kalman Filter (UKF). After conducting the experiments and simulation, the results illustrated the INS/SBL fusion navigation approach was able to reduce the drift problems in the INS. Moreover, UKF showed a better performance than the EKF in the INS

Towards autonomous localization and mapping of AUVs: a survey

Purpose The main purpose of this paper is to investigate two key elements of localization and mapping of Autonomous Underwater Vehicle (AUV), i.e. to overview various sensors and algorithms used for underwater localization and mapping, and to make suggestions for future research. Design/methodology/approach The authors first review various sensors and algorithms used for AUVs in the terms of basic working principle, characters, their advantages and disadvantages. The statistical analysis is carried out by studying 35 AUV platforms according to the application circumstances of sensors and algorithms. Findings As real-world applications have different requirements and specifications, it is necessary to select the most appropriate one by balancing various factors such as accuracy, cost, size, etc. Although highly accurate localization and mapping in an underwater environment is very difficult, more and more accurate and robust navigation solutions will be achieved with the development of both sensors and algorithms. Research limitations/implications This paper provides an overview of the state of art underwater localisation and mapping algorithms and systems. No experiments are conducted for verification. Practical implications The paper will give readers a clear guideline to find suitable underwater localisation and mapping algorithms and systems for their practical applications in hand. Social implications There is a wide range of audiences who will benefit from reading this comprehensive survey of autonomous localisation and mapping of UAVs. Originality/value The paper will provide useful information and suggestions to research students, engineers and scientists who work in the field of autonomous underwater vehicles

Particle Filter Based Ship State and Parameter Estimation for Vessel Maneuvers

Vessel states and parameters estimation is essential for maneuvering and collision avoidance. This study presents an application of particle filter (PF) algorithm to estimate vessel states and parameters. Particularly, to reduce the impact of the vessel’s underactuated property and complex environmental disturbance, the estimation process contains a kinematic curvilinear motion model that describes vessel’s motion. The estimated result can help navigators or ship onboard computers well comprehend the current vessel maneuvering condition. Besides, it can also serve as the necessary data source for vessel’s future trajectory prediction. Therefore, it can be integrated into vessel’s situation awareness (SA) module that supports safety navigation for both conventional and autonomous vessels

Signals and Images in Sea Technologies

Life below water is the 14th Sustainable Development Goal (SDG) envisaged by the United Nations and is aimed at conserving and sustainably using the oceans, seas, and marine resources for sustainable development. It is not difficult to argue that signals and image technologies may play an essential role in achieving the foreseen targets linked to SDG 14. Besides increasing the general knowledge of ocean health by means of data analysis, methodologies based on signal and image processing can be helpful in environmental monitoring, in protecting and restoring ecosystems, in finding new sensor technologies for green routing and eco-friendly ships, in providing tools for implementing best practices for sustainable fishing, as well as in defining frameworks and intelligent systems for enforcing sea law and making the sea a safer and more secure place. Imaging is also a key element for the exploration of the underwater world for various scopes, ranging from the predictive maintenance of sub-sea pipelines and other infrastructure projects, to the discovery, documentation, and protection of sunken cultural heritage. The scope of this Special Issue encompasses investigations into techniques and ICT approaches and, in particular, the study and application of signal- and image-based methods and, in turn, exploration of the advantages of their application in the previously mentioned areas