DRONE-BASED CONTAINER CRANE INSPECTION: CONCEPT, CHALLENGES AND PRELIMINARY RESULTS

Container crane inspection is a very important task to maintain their uninterrupted operation. Nevertheless, this is a costly and time-consuming activity if performed manually. Recently, image-based detection of surface damages or changes using drones has gained increasing interest in industry; especially when objects of interest have a complex structure like container cranes. One main aim of this paper is a single-epoch image analysis which will also serve later for multi-epoch processing. It provides reliable information about current defects that may lead to big damages if not inspected by experts. Naïve Bayes classifier is employed to classify the images in different classes of which critical defects and especially rust is important. The preliminary results show that the precision on the target class reached about 99%. However, 87% percent recall in this class is not enough and it should be improved for this application.Having a large dataset requires an efficient data management system to provide users and decision makers with the information needed. In addition, in order to foster full automation, the aforementioned image analysis component should have a direct connection to the database and thus is able to query image and semantic information. We therefore introduce the second aim of our research, that is a concept for database design. Here, not only the raw data and the final results are integrated but also the intermediate results. At the same time, the database concept is connected to an integrated client interface that allows retrieving data of interest in a virtual globe.

Load Position Detection of Container Crane Using Camera

In this study, the authors proposed an image processing algorithm to detect (measure) the rope length of container crane (distance from camera system to container spreader) and sway angle of the spearder (container). This measurement will be the main input to design the anti-sway control system for container cranes. The image processing algorithm includes the main steps: converting from BGR color space to HSV color space, then, binary image is used to extract the marker area. Next, the Canny boundary detection technique is applied to determine the boundary of the markers in the container spreader. The center location of each marker is determined and used to calculate the distance from the camera system to the container spreader is calculated. The rope length accuracy by the image processing algorithm is 99,79%. It is satisfied for crane control purpose.

Development of a Virtual Reality Simulation for Remote Controlled Container Crane Operator Training

Any large scale cargo port requires the use of container cranes for loading of intermodal containers as well as unloading them. The operator of such a large crane must be appropriately trained to prevent any accidents at the cargo port. This should be achieved by training crane operators on all the protocols of crane operation with significant hours of practice. The aim of this project is to develop a virtual reality (VR) simulation for operator training of container cranes which are remotely controlled, situated at the cargo ports. The simulation consists of a virtual control room equipped with joysticks and display screens, container crane, container ships, trucks and a cargo port environment. We used an actual super post-Panamax quay crane as a reference model for this simulation. The project has been built using Unity3d for the programming and 3DSMAX for the designing of models. The virtual joysticks in the simulation can be interacted by using the Oculus Rift VR setup thus allowing to maneuver the crane and the container load in all three dimensions. This paper mainly discusses the structure and implementation of the virtual reality simulation for crane operator training, along with a proposal for further improvements.

Reinforcement Learning-Based Backstepping Control for Container Cranes

A novel backstepping control scheme based on reinforcement fuzzy Q-learning is proposed for the control of container cranes. In this control scheme, the modified backstepping controller can handle the underactuated system of a container crane. Moreover, the gain of the modified backstepping controller is tuned by the reinforcement fuzzy Q-learning mechanism that can automatically search the optimal fuzzy rules to achieve a decrease in the value of the Lyapunov function. The effectiveness of the applied control scheme was verified by a simulation in Matlab, and the performance was also compared with the conventional sliding mode controller aimed at container cranes. The simulation results indicated that the used control scheme could achieve satisfactory performance for step-signal tracking with an uncertain lope length.

Agent-based modelling and simulation for ship unloading processes: determining the number of trucks and container cranes

Loading and unloading activities generate nearly fifty per cent of the total cost in port. The loading and unloading process of the container at the port is considered as a complex process since it involves several interrelated components, such as ships, cranes, and trucks. The uncertainty of these component activities might impact the loading and unloading time and cost. Agent-based modelling and simulation (ABMS) approach is a method for analyzing and modelling a complex system. This study aims to simulate the unloading process to determine a strategy to reduces unloading process time in the largest port in Indonesian using ABMS approach. The results show that the agent-based simulation approach is feasible to be applied in port activities. This approach can assist decision-makers in predicting the number of facilities that must be used to minimize processing time.

Thorndon Container Wharf: Temporary Works for Recovery of Container Operations (New Zealand)

<p>The Thorndon Container Wharf sustained severe damage in the November 2016 M7.8 Kaikoura earthquake. Substantialworks, of a temporary nature, were required to restore thewharf for container handling operations. The temporary securing works included gravel columns within the reclamation fill and restraining and underpinning of the wharf. All of these works were designed and constructed over a 9-month period to provide a temporary facility for container handling operations for a period of up to 3 years. The temporary securingworks were required to secure the container cranes, maintain support to the wharf structure, and ensure the reclamation behind the wharf had sufficient strength to support lateral loads imposed by the restraining system. This was to enable container operations to recommence and to maintain business continuity, pending action on replacement or reinstatement of the container wharf. This paper outlines the development of the design of the temporary works to secure and return to operations a 125- m working length of wharf and reclamation.</p>

Observer-Based Nonlinear Robust Control of Floating Container Cranes Subject to Output Hysteresis

A container crane mounted on a pontoon is utilized to transfer containers to smaller ships when a large container ship cannot reach the shallow water port. The shipboard container is considered as an underactuated system having complicated kinematic constraints and hysteretic nonlinearities, with only two actuators to conduct simultaneous tasks: tracking the trolley to destination, lifting the container to the desired cable length, and suppressing the axial container oscillations and container swing. Parameter variations, wave-induced motions of the ship, wind disturbance, and nonlinearities remain challenges for control of floating container cranes. To deal with these problems, this study presents the design of two nonlinear robust controllers, taking into account the influence of the output hysteresis, and using velocity feedback from a state observer. Control performance of the proposed controllers is verified in both simulation and experiments. Together with consistently stabilizing outputs, the proposed control approach well rejects hysteresis and disturbance.