ORCID Profile
0000-0002-6900-0901
Current Organisations
The Hong Kong Polytechnic University
,
The University of Newcastle
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Publisher: MDPI AG
Date: 13-10-2016
DOI: 10.3390/S16101696
Publisher: MDPI AG
Date: 09-08-2022
DOI: 10.3390/RS14163857
Abstract: Feature matching is a fundamental procedure in several image processing methods applied in remote sensing. Multispectral sensors with different wavelengths can provide complementary information. In this work, we propose a multispectral line segment matching algorithm based on phase congruency and multiple local homographies (PC-MLH) for image pairs captured by the cross-spectrum sensors (visible spectrum and infrared spectrum) in man-made scenarios. The feature points are first extracted and matched according to phase congruency. Next, multi-layer local homographies are derived from clustered feature points via random s le consensus (RANSAC) to guide line segment matching. Moreover, three geometric constraints (line position encoding, overlap ratio, and point-to-line distance) are introduced in cascade to reduce the computational complexity. The two main contributions of our work are as follows: First, compared with the conventional line matching methods designed for single-spectrum images, PC-MLH is robust against nonlinear radiation distortion (NRD) and can handle the unknown multiple local mapping, two common challenges associated with multispectral feature matching. Second, fusion of line extraction results and line position encoding for neighbouring matching increase the number of matched line segments and speed up the matching process, respectively. The method is validated using two public datasets, CVC-multimodal and VIS-IR. The results show that the percentage of correct matches (PCM) using PC-MLH can reach 94%, which significantly outperforms other single-spectral and multispectral line segment matching methods.
Publisher: IEEE
Date: 30-05-2021
Publisher: IEEE
Date: 21-06-2022
Publisher: MDPI AG
Date: 19-01-2022
Abstract: Visual simultaneous localization and mapping (v-SLAM) and navigation of unmanned aerial vehicles (UAVs) are receiving increasing attention in both research and education. However, extensive physical testing can be expensive and time-consuming due to safety precautions, battery constraints, and the complexity of hardware setups. For the efficient development of navigation algorithms and autonomous systems, as well as for education purposes, the ROS-Gazebo-PX4 simulator was customized in-depth, integrated into our previous released research works, and provided as an end-to-end simulation (E2ES) solution for UAV, v-SLAM, and navigation applications. Unlike most other similar works, which can only stimulate certain parts of the navigation algorithms, the E2ES platform simulates all of the localization, mapping, and path-planning kits in one simulator. The navigation stack performs well in the E2ES test bench with the absolute pose errors of 0.3 m (translation) and 0.9 degree (rotation), respectively, for an 83 m length trajectory. Moreover, the E2ES provides an out-of-box, click-and-fly autonomy in UAV navigation. The project source code is opened for the benefit of the research community.
Publisher: MDPI AG
Date: 25-10-2016
DOI: 10.3390/S16111778
Publisher: American Institute of Aeronautics and Astronautics
Date: 07-01-2018
DOI: 10.2514/6.2018-1919
Publisher: MDPI AG
Date: 30-08-2018
DOI: 10.3390/S18092859
Abstract: This paper presents a model predictive controller (MPC) for position control of a vertical take-off and landing (VTOL) tail-sitter unmanned aerial vehicle (UAV) in hover flight. A ‘cross’ configuration quad-rotor tail-sitter UAV is designed with the capabilities for both hover and high efficiency level flight. The six-degree-of-freedom (DOF) nonlinear dynamic model of the UAV is built based on aerodynamic data obtained from wind tunnel experiments. The model predictive position controller is then developed with the augmented linearized state-space model. Measured and unmeasured disturbance model are introduced into the modeling and optimization process to improve disturbance rejection ability. The MPC controller is first verified and tuned in the hardware-in-loop (HIL) simulation environment and then implemented in an on-board flight computer for real-time indoor experiments. The simulation and experimental results show that the proposed MPC position controller has good trajectory tracking performance and robust position holding capability under the conditions of prevailing and gusty winds.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2020
Publisher: MDPI AG
Date: 07-09-2020
DOI: 10.3390/JMSE8090688
Abstract: The remotely operated vehicles (ROVs) are important to provide the technology support for both the traditional offshore structures and rapidly-growing renewable energy facilities during their full-lifecycles, such as site survey, installation, inspection, maintenance and repair. Regarding the motion and performance of a ROV, the understanding of its hydrodynamic properties is essential when exposing to the disturbances of wave and current. In this study, a numerical model is proposed within the frame of an open-source platform OpenFOAM. The hydrodynamics of the adopted ROV (BlueRov2) in its four principal degrees of freedoms (DOFs) is numerically simulated by a Reynolds-Averaged Navier-Stokes (RANS) solver. Meanwhile, an experimental test is carried out by using a novel technique on measuring the hydrodynamic forces and moments. To validate the numerical prediction methodologies, a set of systematic simulations of the ROV subjected to the disturbances caused by various flow conditions are performed. Comparing to the model test measurement, the numerical model proved to be reliable in offering a good estimation of the hydrodynamic parameters. This also indicates that the presented numerical methodologies and experimental techniques can be applied to other types of open-frame ROVs in quantifying the hydrodynamic parameters, capturing the physics of the fluid-structure interaction (FSI) and feature of the turbulent vorticity which are all essential for the effective control of the ROVs under the nonlinear flow disturbances.
Publisher: MDPI AG
Date: 24-05-2021
Abstract: Having an exciting array of applications, the scope of unmanned aerial vehicle (UAV) application could be far wider one if its flight endurance can be prolonged. Solar-powered UAV, promising notable prolongation in flight endurance, is drawing increasing attention in the industries’ recent research and development. This work arose from a Bachelor’s degree capstone project at Hong Kong Polytechnic University. The project aims to modify a 2-metre wingspan remote-controlled (RC) UAV available in the consumer market to be powered by a combination of solar and battery-stored power. The major objective is to greatly increase the flight endurance of the UAV by the power generated from the solar panels. The power system is first designed by selecting the suitable system architecture and then by selecting suitable components related to solar power. The flight control system is configured to conduct flight tests and validate the power system performance. Under fair experimental conditions with desirable weather conditions, the solar power system on the aircraft results in 22.5% savings in the use of battery-stored capacity. The decrease rate of battery voltage during the stable level flight of the solar-powered UAV built is also much slower than the same configuration without a solar-power system.
Publisher: MDPI AG
Date: 30-06-2020
DOI: 10.3390/DATA5030057
Abstract: Hydrodynamic forces are an important input value for the design, navigation and station keeping of underwater Remotely Operated Vehicles (ROVs). The experiment investigated the forces imparted by currents (with representative real world turbulence) and waves on a commercially available ROV, namely the BlueROV2 (Blue Robotics, Torrance, USA). Three different distances of a simplified cylindrical obstacle (shading effects) were investigated in addition to the free stream cases. Eight tethers held the ROV in the middle of the 2 m water depth to minimise the influence of the support structure without completely restricting the degrees of freedom (DoF). Each tether was equipped with a load cell and small motions and rotations were documented with an underwater video motion capture system. The paper describes the experimental set-up, input values (current speed and wave definitions) and initial processing of the data. In addition to the raw data, a processed dataset is provided, which includes forces in all three main coordinate directions for each mounting point synchronised with the 6DoF results and the free surface elevations. The provided dataset can be used as a validation experiment as well as for testing and development of an algorithm for position control of comparable ROVs.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 04-2020
Publisher: MDPI AG
Date: 16-02-2021
DOI: 10.3390/S21041385
Abstract: The inspection of electrical and mechanical (E& M) devices using unmanned aerial vehicles (UAVs) has become an increasingly popular choice in the last decade due to their flexibility and mobility. UAVs have the potential to reduce human involvement in visual inspection tasks, which could increase efficiency and reduce risks. This paper presents a UAV system for autonomously performing E& M device inspection. The proposed system relies on learning-based detection for perception, multi-sensor fusion for localization, and path planning for fully autonomous inspection. The perception method utilizes semantic and spatial information generated by a 2-D object detector. The information is then fused with depth measurements for object state estimation. No prior knowledge about the location and category of the target device is needed. The system design is validated by flight experiments using a quadrotor platform. The result shows that the proposed UAV system enables the inspection mission autonomously and ensures a stable and collision-free flight.
Publisher: MDPI AG
Date: 20-08-2022
Abstract: A dynamic model that considers both linear and complex nonlinear effects extensively benefits the model-based controller development. However, predicting a detailed aerodynamic model with good accuracy for unmanned aerial vehicles (UAVs) is challenging due to their irregular shape and low Reynolds number behavior. This work proposes an approach to model the full translational dynamics of a quadrotor UAV by a feedforward neural network, which is adopted as the prediction model in a model predictive controller (MPC) for precise position control. The raw flight data are collected by tracking various pre-designed trajectories with PX4 autopilot. The neural network model is trained to predict the linear accelerations from the flight log. The neural network-based model predictive controller is then implemented with the automatic control and dynamic optimization toolkit (ACADO) to achieve real-time online optimization. Software in the loop (SITL) simulation and indoor flight experiments are conducted to verify the controller performance. The results indicate that the proposed controller leads to a 40% reduction in the average trajectory tracking error compared to the traditional PID controller.
Publisher: IEEE
Date: 06-2019
Publisher: MDPI AG
Date: 02-10-2020
DOI: 10.3390/S20195651
Abstract: This paper presents a control allocation method for enhancing the attitude following performance and the energy efficiency of a variable-pitch propeller (VPP) system on quadrotor-based unmanned aerial vehicles. The VPP system was modeled according to the blade element momentum (BEM) theory, and an actuator allocation method was developed with the aim of enhancing the attitude control and energy performance. A simulation environment was built to validate the VPP system by creating a thrust and moment database from the experiments. A four-motor variable-pitch quadrotor was built for verifying the proposed method. The control allocation method was firstly verified in a simulation environment, and was then implemented in a flight controller for indoor flight experiments. The simulation results show the proposed control allocation method greatly improves the yaw following performance. The experimental results demonstrate a difference in the energy consumption through various pitch angles, as well as a reduction in energy consumption, by applying this VPP system.
Publisher: American Institute of Aeronautics and Astronautics
Date: 05-01-2017
DOI: 10.2514/6.2017-0811
Publisher: Elsevier BV
Date: 12-2018
Publisher: Elsevier BV
Date: 08-2021
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2020
Publisher: MDPI AG
Date: 29-09-2023
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2020
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2021
Publisher: Elsevier BV
Date: 10-2019
Publisher: IEEE
Date: 11-12-2022
Publisher: MDPI AG
Date: 27-11-2021
DOI: 10.3390/S21237888
Abstract: The ever-burgeoning growth of autonomous unmanned aerial vehicles (UAVs) has demonstrated a promising platform for utilization in real-world applications. In particular, a UAV equipped with a vision system could be leveraged for surveillance applications. This paper proposes a learning-based UAV system for achieving autonomous surveillance, in which the UAV can be of assistance in autonomously detecting, tracking, and following a target object without human intervention. Specifically, we adopted the YOLOv4-Tiny algorithm for semantic object detection and then consolidated it with a 3D object pose estimation method and Kalman filter to enhance the perception performance. In addition, UAV path planning for a surveillance maneuver is integrated to complete the fully autonomous system. The perception module is assessed on a quadrotor UAV, while the whole system is validated through flight experiments. The experiment results verified the robustness, effectiveness, and reliability of the autonomous object tracking UAV system in performing surveillance tasks. The source code is released to the research community for future reference.
Location: United Kingdom of Great Britain and Northern Ireland
No related grants have been discovered for Boyang Li.