Daniel Toal

This paper presents a comprehensive overview of cutting-edge autonomous forklifts, with a strong emphasis on sensors, object detection and system functionality. It aims to explore how this technology is evolving and where it is likely headed in both the near and long-term future, while also highlighting the latest developments in both academic research and industrial applications. Given the critical importance of object detection and recognition in machine vision and autonomous vehicles, this area receives particular attention. The article provides an in-depth summary of both commercial and prototype forklifts, discussing key aspects such as design features, capabilities and benefits, and offers a detailed technical comparison. Specifically, it clarifies that all available data pertains to commercially available forklifts. To obtain a better understanding of the current state-of-the-art and its limitations, the analysis also reviews commercially available autonomous forklifts. Finally, this paper includes a comprehensive bibliography of research findings in this field.

This paper presents a start to finish approach on the inspection and repair of damaged ship hulls in water. The repair of ship hulls in water has been made viable by recent developments in Friction Stir Welding (FSW) of steels. The project is a European Union (EU) funded collaboration involving a variety of academic and industry partners aiming to increase the productivity of small and medium sized EU shipyards. This paper outlines the overall procedure of a repair mission while focusing on the inspection task by use of Remotely Operated Vehicles (ROV), as performed in a variety of environments on a fractured plate made from ship grade steel. The equipment and processing procedures are detailed with discussion demonstrating the capabilities in generating the required quality of 3D models in order for defects to be identified and for a sufficiently sized repair patch to be designed.

An investigation of the effects of wind gusts on the directly interconnected wind generators is reported, and techniques toward the mitigation of the wind gust negative influences have been proposed. Using a directly interconnected system approach, wind turbine generators are connected to a single synchronous bus or collection grid without the use of power converters on each turbine. This bus can then be transformed for transmission onshore using High Voltage Alternating Current, Low-Frequency Alternating Current or High Voltage Direct Current techniques with shared power conversion resources onshore connecting the farm to the grid. Analysis of the potential for instability in transient conditions on the wind farm, for example, caused by wind gusts is the subject of this paper. Gust magnitude and rise time/fall time are investigated. Using pitch control and the natural damping of the high inertial offshore system, satisfactory overall system performance and stability can be achieved during these periods of transience.

The conventional fuzzy c-spherical shells (FCSS) clustering model is extended to cluster shells involving non-crisp numbers, in this paper. This is achieved by a vectorized representation of distance, between two non-crisp numbers like the crisp numbers case. Using the proposed clustering method, named vector fuzzy c-spherical shells (VFCSS), all crisp and non-crisp numbers can be clustered by the FCSS algorithm in a unique structure. Therefore, we can implement FCSS clustering over various types of numbers in a unique structure with only a few alterations in the details used in implementing each case. The relations of VFCSS applied to crisp and non-crisp (containing symbolic-interval, LR-type, TFN-type and TAN-type fuzzy) numbers are presented in this paper. Finally, simulation results are reported for VFCSS applied to synthetic LR-type fuzzy numbers; where the application of the proposed method in real life and in geomorphology science is illustrated by extracting the radii of circular agricultural fields using remotely sensed images and the results show better performance and lower cost computational complexity of the proposed method in comparison to conventional FCSS.

Industry 4.0 uses the analysis of real-time data, artificial intelligence, automation, and the interconnection of components of the production lines to improve manufacturing efficiency and quality. Manufacturing Execution Systems (MESs) and Autonomous Intelligent Vehicles (AIVs) are key elements of Industry 4.0 implementations. An MES connects, monitors, and controls data flows on the factory floor, while automation is achieved by using AIVs. The Robot Operating System (ROS) built AIVs are targeted here. To facilitate MES and AIV interactions, there is a need to integrate the MES and the AIVs to help in building an automated and interconnected manufacturing environment. This integration needs middleware, which understands both MES and AIVs. To address this issue, a LabVIEW-based scheduler is proposed here as the middleware. LabVIEW communicates with the MES through webservices and has support for ROS. The main task of the scheduler is to control the AIV based on MES requests. The scheduler developed was tested in a real factory environment using the SAP MES and a Robotnik ‘RB-1′ robot. The scheduler interface provides real-time information about the current status of the MES, AIV, and the current stage of scheduler processing. The proposed scheduler provides an efficient automated product delivery system that transports the product from process cell to process cell using the AIV, based on the production sequences defined by the MES. In addition, using the proposed scheduler, integration of an MES is possible with any low-cost ROS-built AIV.

Feature extraction and matching is a key component in image stitching and a critical step in advancing image reconstructions, machine vision and robotic perception algorithms. This paper presents a fast and robust underwater image mosaicking system based on (2D)²PCA and A-KAZE key-points extraction and optimal seam-line methods. The system utilizes image enhancement as a preprocessing step to improve quality and allow for greater keyframe extraction and matching performance, leading to better quality mosaicking. The application focus of this paper is underwater imaging and it demonstrates the suitability of the developed system in advanced underwater reconstructions. The results show that the proposed method can address the problems of noise, mismatching and quality issues which are typically found in underwater image datasets. The results demonstrate the proposed method as scale-invariant and show improvements in terms of processing speed and system robustness over other methods found in the literature.

The overall control system for an open-frame Remotely Operated Vehicle (ROV) is typically built from three subsystems: guidance, navigation and control (GNC). The control allocation plays a vital role in the control subsystem. Typically, open-frame underwater vehicles have p actuators (thrusters) for the motion in the horizontal plane, and the control allocation problem, in this case, is very complex and hard to visualise, because the normalised constrained control subset is a p-dimensional unit cube. The aim of this paper is to give a clear picture and a geometric interpretation of the problem and to introduce a hybrid method, based on the integration of a weighted pseudoinverse and the fixed-point method. The main idea of the hybrid method is visualised, and the deep geometric insight is provided using a “virtual” ROV in low-dimensional control spaces, including visualisation of the attainable command set, solution lines, control energy spheres and the role of pseudoinverse and fixed-point iterations. The same concepts are then extended to higher-dimensional cases, for open-frame ROV with four X-shaped (vectored) horizontal thrusters, which is one of the most common thruster configurations for commercial ROVs. The proposed hybrid method has been developed, integrated into a generic fault-tolerant ROV control system and evaluated in virtual and real-world environments off the west coast of Ireland using observation-class ROV Latis and work-class ROV Étaín.

This paper presents a docking station heave motion prediction method for dynamic remotely operated vehicle (ROV) docking, based on the Adaptive Neuro-Fuzzy Inference System (ANFIS). Due to the limited power onboard the subsea vehicle, high hydrodynamic drag forces, and inertia, work-class ROVs are often unable to match the heave motion of a docking station suspended from a surface vessel. Therefore, the docking relies entirely on the experience of the ROV pilot to estimate heave motion, and on human-in-the-loop ROV control. However, such an approach is not available for autonomous docking. To address this problem, an ANFIS-based method for prediction of a docking station heave motion is proposed and presented. The performance of the network was evaluated on real-world reference trajectories recorded during offshore trials in the North Atlantic Ocean during January 2019. The hardware used during the trials included a work-class ROV with a cage type TMS, deployed using an A-frame launch and recovery system.

In recent years, we have seen significant interest in the use of permanently deployed resident robotic vehicles for commercial inspection, maintenance and repair (IMR) activities. This paper presents a concept and demonstration, through offshore trials, of a low-cost, low-maintenance, navigational marker that can eliminate drift in vehicle INS solution when the vehicle is close to the IMR target. The subsea localisation marker system is fixed on location on the resident field asset and is used in on-vehicle machine vision algorithms for pose estimation and facilitation of a high-resolution world coordinate frame registration with a high refresh rate. This paper presents evaluation of the system during trials in the North Atlantic Ocean during January 2019. System performances and propagation of position error is inspected and estimated, and the effect of intermittent visual based position update to Kalman filter and onboard INS solution is discussed. The paper presents experimental results of the commercial state-of-the-art inertial navigation system operating in the pure inertial mode for comparison.

ZnO, which has high electrochemical stability, wide band gap energy, large excitonic binding energy, intense near band excitonic emission and is non-toxic, have potential applications in all fields. This chapter reviews the structural, optical and electrical properties of undoped and doped ZnO thin films. The type of doping highly influences the structural properties such as grain size, texture coefficient and unit cell properties. The dopants of transition metal and nonmetals have unique characteristics. Moreover, mono-doping and co-doping encourage this research. The optical properties such as bandgap, charge carrier concentrations and transmissions of the films depend on the doping as well as the preparation condition of the films. The effect of doping on its properties is also discussed.