深度学习在无人船中的应用与挑战

Autonomous surface ships have become increasingly interesting for commercial maritime sectors.Before deep learning(DL)was proposed,surface ship autonomy was mostly model-based.The development of artificial intelligence(AI)has prompted new challenges in the maritime industry.A detailed literature study and examination of DL applications in autonomous surface ships are still missing.Thus,this article reviews the current progress and applications of DL in the field of ship autonomy.The history of different DL methods and their application in autonomous surface ships is briefly outlined.Then,the previously published works studying DL methods in ship autonomy have been categorized into four groups,i.e.,control systems,ship navigation,monitoring system,and transportation and logistics.The state-of-the-art of this review paper majorly lies in presenting the existing limitations and innovations of different applications.Subsequently,the current issues and challenges for DL application in autonomous surface ships are discussed.In addition,we have proposed a comparative study of traditional and DL algorithms in ship autonomy and also provided the future research scope as well.

Scalable Distributed Sensor Fault Diagnosis for Smart Buildings

The enormous energy use of the building sector and the requirements for indoor living quality that aim to improve occupants' productivity and health, prioritize Smart Buildings as an emerging technology. The Heating, Ventilation and Air-Conditioning(HVAC) system is considered one of the most critical and essential parts in buildings since it consumes the largest amount of energy and is responsible for humans comfort. Due to the intermittent operation of HVAC systems, faults are more likely to occur, possibly increasing eventually building's energy consumption and/or downgrading indoor living quality. The complexity and large scale nature of HVAC systems complicate the diagnosis of faults in a centralized framework. This paper presents a distributed intelligent fault diagnosis algorithm for detecting and isolating multiple sensor faults in large-scale HVAC systems.Modeling the HVAC system as a network of interconnected subsystems allows the design of a set of distributed sensor fault diagnosis agents capable of isolating multiple sensor faults by applying a combinatorial decision logic and diagnostic reasoning. The performance of the proposed method is investigated with respect to robustness, fault detectability and scalability. Simulations are used to illustrate the effectiveness of the proposed method in the presence of multiple sensor faults applied to a 83-zone HVAC system and to evaluate the sensitivity of the method with respect to sensor noise variance.

Cooperative Multi-Agent Control for Autonomous Ship Towing Under Environmental Disturbances

Among the promising application of autonomous surface vessels(ASVs)is the utilization of multiple autonomous tugs for manipulating a floating object such as an oil platform,a broken ship,or a ship in port areas.Considering the real conditions and operations of maritime practice,this paper proposes a multi-agent control algorithm to manipulate a ship to a desired position with a desired heading and velocity under the environmental disturbances.The control architecture consists of a supervisory controller in the higher layer and tug controllers in the lower layer.The supervisory controller allocates the towing forces and angles between the tugs and the ship by minimizing the error in the position and velocity of the ship.The weight coefficients in the cost function are designed to be adaptive to guarantee that the towing system functions well under environmental disturbances,and to enhance the efficiency of the towing system.The tug controller provides the forces to tow the ship and tracks the reference trajectory that is computed online based on the towing angles calculated by the supervisory controller.Simulation results show that the proposed algorithm can make the two autonomous tugs cooperatively tow a ship to a desired position with a desired heading and velocity under the(even harsh)environmental disturbances.

Robustifying Dynamic Positioning of Crane Vessels for Heavy Lifting Operation

Construction crane vessels make use of dynamic positioning(DP)systems during the installation and removal of offshore structures to maintain the vessel’s position.Studies have reported cases of instability of DP systems during offshore operation caused by uncertainties,such as mooring forces.DP"robustification"for heavy lift operations,i.e.,handling such uncertainties systematically and with stability guarantees,is a long-standing challenge in DP design.A new DP method,composed by an observer and a controller,is proposed to address this challenge,with stability guarantees in the presence of uncertainties.We test the proposed method on an integrated cranevessel simulation environment,where the integration of several subsystems(winch dynamics,crane forces,thruster dynamics,fuel injection system etc.)allow a realistic validation under a wide set of uncertainties.