Daibing Zhang

Autonomous landing of an UAV with a ground-based actuated infrared stereo vision system

In this study, we focus on the problem of landing an unmanned aerial vehicle (UAV) in unknown and Global Navigation Satellite System(GNSS)-denied environments based on an infrared stereo vision system. This system is fixed on the ground and used to track the UAVs position during the landing process. In order to enlarge the search field of view (FOV), a pan-tilt unit (PTU) is employed to actuate the vision system. The infrared camera is chosen as the exteroceptive sensor for two main reasons: first, it can be used under all weather conditions and around the clock; second, infrared targets can be tracked based on infrared spectrum features at a lower computational cost compared to tracking texture features in visible spectrum. State-of-the-art active contour based algorithms and the mean shift algorithm have been evaluated with regard to detecting and tracking an infrared target. Field experiments have been carried out using an unmanned quadrotor and a fixed-wing unmanned aircraft, with both qualitative and quantitative evaluations. The results demonstrate that our system can track UAVs without artificial markers and is sufficient to enhance or replace the GNSS-based localization in GNSS-denied environment or where its information is inaccurate.

Vision-based autonomous landing system for unmanned aerial vehicle: A survey

Recently, there has been growing interest in developing unmanned aircraft system (UAS) based on visual sensors. During the whole autonomous assignment, the landing procedure is one of the most dangerous and challenging process. For most of unmanned aircraft vehicle, visual sensors are the basic equipment, which are also widely used during the landing maneuver. This paper first presents the main research groups involved in the development of vision-based autonomous landing systems. Then it discusses the detail of each algorithms and systems in different categories. The goal of this paper is to review the state-of-the-art vision-based autonomous landing methods that captures all milestones and seminal works. These algorithms and systems are classified into different categories. Finally, the paper highlights challenges in this research field.

Supervised Neural Q_learning based Motion Control for Bionic Underwater Robots

Bionic underwater robots have been a hot research area in recent years. The motion control methods for a kind of bionic underwater robot with two undulating fins are discussed in this paper. The equations of motion for the bionic underwater robot are described. To apply the reinforcement learning to the actual robot control, a Supervised Neural Q_learning (SNQL) algorithm is put forward. This algorithm is based on conventional Q_learning algorithm, but has three remarkable distinctions: (1) using a feedforward neural network to approximate the Q_function table; (2) adopting a learning sample database to speed up learning and improve the stability of learning system; (3) introducing a supervised control in the earlier stage of learning for safety and to speed up learning again. Experiments of swimming straightforward are carried out with SNQL algorithm. Results indicate that the SNQL algorithm is more effective than pure neural Q_learning or supervised control. It is a feasible approach to figure out the motion control for bionic underwater robots.

A ground-based optical system for autonomous landing of a fixed wing UAV

This paper presents a new ground-based visual approach for guidance and safe landing of an unmanned aerial vehicle (UAV) in Global Navigation Satellite System(GNSS)-denied environments. In our previous work, the old system consists of one pan-tilt unit(PTU) with two cameras, whose detection range is limited by the baseline. To achieve long-range detection and cover wide field of regard, we mounted two separate sets of PTU integrated with visible light camera on both sides of the runway instead of our previous assembled stereo vision system. Then, the well-known AdaBoost method was evaluated with regard to detecting and tracking the target. To achieve the relative position between the UAV and landing area, we used triangulation to calculate the 3D coordinates of the UAV. By combining the estimated position in the closed loop control, we obtain the autonomous landing strategy. Finally, we present several real flights in outdoor environments, and compare its accuracy with ground truth provided by GNSS. The results support the validity and accuracy of the presented system.

Vision-based Detection and Tracking of a Mobile Ground Target Using a Fixed-wing UAV

This paper presents a framework for tracking a mobile ground target (MGT) using a fixed-wing unmanned aerial vehicle (UAV). Challenges from pure theories to practical applications, including varying illumination, computational limits and a lack of clarity are considered. The procedure consists of four steps, namely: target detection, target localization, states estimation and UAV guidance. Firstly, the MGT in the wild is separated from the background using a Laplacian operator-based method. Next, the MGT is located by performing coordinate transformations with the assumption that the altitude of the ground is invariant and known. Afterwards, a Kalman filter is used to estimate the location and velocity of the MGT. Finally, a modified guidance law is developed to guide the UAV to circle and track the MGT. The performance of our framework is validated by simulations and a number of actual flight tests. The results indicate that the framework is effective and of low computational complexity, and in particular our modified guidance law can reduce the error of the tracking distance by about 75% in specified situations. With the proposed framework, such challenges caused by the actual system can be tackled effectively, and the fixed-wing UAV can track the MGT stably.

Design of a Central Pattern Generator for Bionic-robot Joint with Angular Frequency Modulation

The paper proposes an artificial central pattern generator (CPG) for bionic-robot joint control. The neural oscillator adopted to produce rhythmic pattern is specially designed from original sin-cosine oscillator model. An amplitude neural estimator consisted of two neurons is presented to provide sensor feedback to CPG control. The artificial CPG can adapt itself to the physical system parameters variety by rhythmic movement angular frequency modulation.

Computational Hydrodynamics and Statistical Modeling on Biologically Inspired Undulating Robotic Fins: A Two-Dimensional Study

Undulation fishes, whose propulsion is mainly achieved by undulating ribbon fins, are good at maneuvering or stabilizing at low speeds. This paper suggests and proposes a two-dimensional approximate computational model, which is used to conduct an initial analysis on undulation propulsion scheme. It is believed that this undulating mode has a better potential for exploitation in artificial underwater systems. Hydrodynamics of two-dimensional undulating fins under a series of kinematical parameter sets is explored via numerical simulation. The periodicity of undulation forces and moments is studied. The effects of inlet velocity, wavelength, undulation frequency, and undulation amplitude are investigated. Furthermore, a dimensionless two-parameter model for undulation surge force is established with a given wavelength (in terms of, a single wavelength or a dual wavelength) using statistical method. The work in this paper is able to provide studies on bionic undulation mode. It has also formed a meaningful basis for three-dimensional (3D) hydrodynamics and corresponding control methods in bionic undulation robots.

Effective motion control of the biomimetic undulating fin via iterative learning

The biomimetic undulating fin, RoboGnilos, is inspired by natural fish that generally swim via undulations of a long dorsal or anal fin. However, the present performance of this fin-type underwater propulsor can hardly be satisfactory in velocity, efficiency, or maneuverability, and retains a long distance to practical applications. This paper examines the dynamics of the undulating fin, and proposes an iterative learning approach based motion control to improve its steady propulsion velocity. This iterative learning controller is cooperated with a filter, to reduce the measurement noise, and a curve fitting component, to keep the necessary phase difference between neighbored fin rays. The detailed iterative learning based motion control algorithm is designed and implemented in the biomimetic undulating fin. The experimental results validate that the proposed learning motion control can effectively improve the propulsion of RoboGnilos. For instance, the steady propulsion velocity may be enhanced by over 40% with specified parameters.

Localization Framework for Real-Time UAV Autonomous Landing: An On-Ground Deployed Visual Approach

One of the greatest challenges for fixed-wing unmanned aircraft vehicles (UAVs) is safe landing. Hereafter, an on-ground deployed visual approach is developed in this paper. This approach is definitely suitable for landing within the global navigation satellite system (GNSS)-denied environments. As for applications, the deployed guidance system makes full use of the ground computing resource and feedbacks the aircraft’s real-time localization to its on-board autopilot. Under such circumstances, a separate long baseline stereo architecture is proposed to possess an extendable baseline and wide-angle field of view (FOV) against the traditional fixed baseline schemes. Furthermore, accuracy evaluation of the new type of architecture is conducted by theoretical modeling and computational analysis. Dataset-driven experimental results demonstrate the feasibility and effectiveness of the developed approach.

A ground-based multi-sensor system for autonomous landing of a fixed wing UAV

This paper presents a novel ground-based multi-sensor system for guidance and safe landing of a fixed-wing Unmanned Aerial Vehicle (UAV). Unlike with our previous works, this newly developed system is based on one Pan/Tilt Unit (PTU) with visible light camera laser pointer and Ultra-wideband (UWB) radar. To simplify the calibration process in the wide area, a DGPS-based calibration method was designed. As a compensation for camera, we integrated UWB radar module to achieve higher accuracy of localization and implemented laser pointer with retroreflector to enhance the target feature in the distance, which extends the detection range. Then, we used Canny edge detector and Hough transform to track the UAV in the air. Finally, several field experiments with small- and middle-size platforms demonstrate that our system has the ability to acquire the target from a long range and meet the requirements of robustness and real-time capability. Therefore, the proposed system is suitable to complement or replace the GNSS-based position estimation in situations where GNSS information is unavailable or inaccurate.