MPC-based Motion Planning and Control Enables Smarter and Safer Autonomous Marine Vehicles:Perspectives and a Tutorial Survey

Autonomous marine vehicles(AMVs)have received considerable attention in the past few decades,mainly because they play essential roles in broad marine applications such as environmental monitoring and resource exploration.Recent advances in the field of communication technologies,perception capability,computational power and advanced optimization algorithms have stimulated new interest in the development of AMVs.In order to deploy the constrained AMVs in the complex dynamic maritime environment,it is crucial to enhance the guidance and control capabilities through effective and practical planning,and control algorithms.Model predictive control(MPC)has been exceptionally successful in different fields due to its ability to systematically handle constraints while optimizing control performance.This paper aims to provide a review of recent progress in the context of motion planning and control for AMVs from the perceptive of MPC.Finally,future research trends and directions in this substantial research area of AMVs are highlighted.

Design and Control of a DC Boost Converter for Fuel-Cell-Powered Marine Vehicles

Economic factors along with legislation and policies to counter harmful pollution apply specifically to maritime drive research for improved power generation and energy storage.Proton exchange membrane fuel cells are considered among the most promising options for marine applications.Switching converters are the most common interfaces between fuel cells and all types of load in order to provide a stable regulated voltage.In this paper,a method using artificial neural networks(ANNs)is developed to control the dynamics and response of a fuel cell connected with a DC boost converter.Its capability to adapt to different loading conditions is established.Furthermore,a cycle-mean,black-box model for the switching device is also proposed.The model is centred about an ANN,too,and can achieve considerably faster simulation times making it much more suitable for power management applications.

Multi-role Remotely Operated Marine Surface Vehicle

Development of man-packable,versatile marine surface vehicle with ability to rescue a drowning victim and also capable of carrying mission specific sensor is explored.Design thinking methodology is implemented by using existing equipment/platform with the addition of external attachment to make it a functional product.Iterative prototyping process with extensive testing to achieve user-centric solution.Individual prototypes and their possible sub-configurations with their integration and characteristics are studied and compared with numerical model,inferences obtained are utilised to improve for the next iteration.A novel hinge-clamp assembly enables this marine surface vehicle to operate in the event of an overturn,this phenomenon is further studied with the aid of a mathematical model(Pendulum in a fluid).This research project aims to demonstrate a multi-role unmanned surface vehicle.

MCMOD:The Multi-Category Large-Scale Dataset for Maritime Object Detection

The marine environment is becoming increasingly complex due tothe various marine vehicles,and the diversity of maritime objects poses a challengeto marine environmental governance.Maritime object detection technologyplays an important role in this segment.In the field of computer vision,there is no sufficiently comprehensive public dataset for maritime objects inthe contrast to the automotive application domain.The existing maritimedatasets either have no bounding boxes(which are made for object classification)or cover limited varieties of maritime objects.To fulfil the vacancy,this paper proposed the Multi-Category Large-Scale Dataset for MaritimeObject Detection(MCMOD)which is collected by 3 onshore video camerasthat capture data under various environmental conditions such as fog,rain,evening,etc.The whole dataset consists of 16,166 labelled images alongwith 98,590 maritime objects which are classified into 10 classes.Comparedwith the existing maritime datasets,MCMOD contains a relatively balancedquantity of objects of different sizes(in the view).To evaluate MCMOD,this paper applied several state-of-the-art object detection approaches fromcomputer vision research on it and compared their performances.Moreover,a comparison between MCMOD and an existing maritime dataset was conducted.Experimental results indicate that the proposed dataset classifies moretypes of maritime objects and covers more small-scale objects,which canfacilitate the trained detectors to recognize more types of maritime objects anddetect maritime objects over a relatively long distance.The obtained resultsalso showthat the adopted approaches need to be further improved to enhancetheir capabilities in the maritime domain.

Rank Reliability Assessment of the Technical Object at Early Design Stage with Limited Operational Data - A Case Study

Construction conception of an object requires multi-criterion analysis. In such a case, reliability analy.sis gives rough information on availability and fulfillment of main functions. In the paper, the analysis of drive system in river barge pusher is presented. It consists of Reliability Block Diagram （RBD） analysis of various composition of the system and Markov analysis based on prior estimated operational data.