Zhen Ziyang

Bio: Zhen Ziyang is an academic researcher from Nanjing University of Aeronautics and Astronautics. The author has contributed to research in topics: Quadcopter & Ant colony optimization algorithms. The author has an hindex of 5, co-authored 17 publications receiving 68 citations.

Tiltrotor aircraft attitude control in conversion mode based on optimal preview control

Abstract: An attitude control method based on optimal preview control for tiltrotor aircraft in conversion mode is proposed in this paper. A nonlinear model and a linear mathematical model for a tiltrotor aircraft are given. For the attitude control problem of the aircraft, a preview controller based on the error system model is designed, which produces seven control variables signals to control the pitch, roll and yaw movements of the aircraft. The controller is composed of two parts, one is feedforward loop which fuses the future previewed information, the other is feedback loop which equals to the optimal state feedback control. Then a Dryden turbulence is added to impact the aircraft in simulation. The simulation results show that the previewed information is beneficial for improving the accuracy of attitude tracking control and capacity of resisting disturbance.

Path planning of UAVs formation based on improved ant colony optimization algorithm

Abstract: Aimed at the problem about the path planning of multiple unmanned aerial vehicles (UAVs) formation in radar-threatening environment, an intelligent method based on improved ant colony optimization (ACO) algorithm is presented To begin with, initialize the mission environment before the mathematically modeling of flight path and environment Then, a path of a single UAV can be sought by ACO algorithm The following step is the formation of multiple UAVs Ultimately, a real flyable path comes out on condition that the formation remains unchanged However, path planning based on the traditional ACO may not be the best when it comes to the fact that it may contain some dispensable routes To solve this problem, the path is trimmed afterwards to make it more straightforward Simulation results show that the proposed improved intelligent method have better dynamic nature and computing power

Monte Carlo approach to the analysis of UAVs control system

Abstract: Several nonlinear control theories have been implied in UAVs control system, although all of those control system can achieve control objects and satisfy control requirement, the robustness of those system is unknown. This paper introduces Monte Carlo approach which is a statistical method to the study of UAVs control system's robustness. Disturbances which are worthy to consider and detailed process are presented. And Simulation results show that, considering atmospheric turbulence and sensors noises, Monte Carlo approach is a proper tool in the analysis of the robustness of UAVs formation control system.

3D scene simulation of UAVs formation flight based on FlightGear simulator

Abstract: To solve the problem of scene simulation for unmanned aerial vehicles (UAVs) formation flight, a method based on FlightGear is proposed. The 3D scene simulation system scheme is designed. The formation flight control system of leader-followers is established by the Matlab/Simulink, where the flight state data of UAVs is output to the FlightGear simulator by the interface of Matlab. A 3D model of UAV is designed based on AC3D software. A friendly man-machine interface is designed, which combines the simulation data with 3D animations, to make the simulation results more exhaustive. Finally, the UAVs formation flight scene simulation results show the effectiveness of the proposed method.

Information fusion distributed navigation for UAVs formation flight

Abstract: High precision navigation is a key technique for unmanned aerial vehicles (UAVs) formation flight. A methodology for fusing data from inertial navigation system (INS), global positioning system (GPS) and vision sensor is presented. For the lead UAV, a Kalman filter based INS/GPS integrated navigation system is designed. For the wing UAV, a local Kalman filter based INS/GPS integrated subsystem and local Kalman filter based INS/Vision/JTIDS subsystem are designed, and a distributed master filter based on information fusion estimation is designed to get the absolute navigation solution of wing UAV. And then, the relative navigation between the lead UAV and the wing UAV in formation can be obtained. Simulation results verify the validity of this formation navigation architecture for UAVs. Moreover, it illustrates that the distributed information fusion INS/GPS/Vision integrated navigation system for formation UAVs has highest geodetic navigation accuracy, when comparing with INS/GPS integrated navigation system and INS/Vision integrated navigation system.

Survey on computational-intelligence-based UAV path planning

Abstract: The key objective of unmanned aerial vehicle (UAV) path planning is to produce a flight path that connects a start state and a goal state while meeting the required constraints. Computational intelligence (CI) is a set of nature-inspired computational methodologies and approaches for addressing complex real-world problems for which mathematical or traditional modelling does not perform well. It has been applied in the field of UAVs since it can yield effective, accurate and rapid solutions. This article provides an overview of studies on UAV path planning based on CI methods published in major journals and conference proceedings. We survey relevant studies with respect to different CI algorithms utilized in UAV path planning, the types of time domain in UAV path planning, namely, offline and online, and the types of environment models, namely, 2D and 3D. It is observed that CI methods outperform traditional methods on online and 3D problems. The analysis is useful for identifying key results from UAV path planning research and is leveraged in this article to highlight trends and open issues.

Survey of computer vision algorithms and applications for unmanned aerial vehicles

Abstract: This paper presents a complete review of computer vision algorithms and vision-based intelligent applications, that are developed in the field of the Unmanned Aerial Vehicles (UAVs) in the latest decade. During this time, the evolution of relevant technologies for UAVs; such as component miniaturization, the increase of computational capabilities, and the evolution of computer vision techniques have allowed an important advance in the development of UAVs technologies and applications. Particularly, computer vision technologies integrated in UAVs allow to develop cutting-edge technologies to cope with aerial perception difficulties; such as visual navigation algorithms, obstacle detection and avoidance and aerial decision-making. All these expert technologies have developed a wide spectrum of application for UAVs, beyond the classic military and defense purposes. Unmanned Aerial Vehicles and Computer Vision are common topics in expert systems, so thanks to the recent advances in perception technologies, modern intelligent applications are developed to enhance autonomous UAV positioning, or automatic algorithms to avoid aerial collisions, among others. Then, the presented survey is based on artificial perception applications that represent important advances in the latest years in the expert system field related to the Unmanned Aerial Vehicles. In this paper, the most significant advances in this field are presented, able to solve fundamental technical limitations; such as visual odometry, obstacle detection, mapping and localization, et cetera. Besides, they have been analyzed based on their capabilities and potential utility. Moreover, the applications and UAVs are divided and categorized according to different criteria.

Multi-UAV Formation Control Based on a Novel Back-Stepping Approach

Abstract: This paper addressed a well-documented open problem regarding multiple unmanned aerial vehicles (multi-UAVs) formation keeping. A cooperative guidance control method based on the back-stepping approach is proposed to rapidly form the desired formation and reach the multi-UAV steady state. The UAV formation system consists of four UAVs, forming a regular triangle formation. One of the UAVs is a virtual leader and is located at the center of the triangle; the other three UAVs are located at the vertices of the triangle. The forward speed of leader is used as the forward direction of the formation, and the followers follow the leader in a formation flight. The error dynamics model of each follower is established by using the leader guidance mechanism, and the communication mode between any two UAVs is established based on graph theory. In addition, the guidance control law is obtained via the back-stepping approach. We reasonably construct the Lyapunov function to prove the stability of the proposed cooperative guidance control law in formation aggregation and keeping. Moreover, the proposed method is compared with the model prediction method (MPC) and the Lyapunov method to further verify the effectiveness of the proposed method. The simulation results show that each UAV can converge to the desired motion trajectory and fly in the desired formation with fast convergence speed and small steady-state error.

A Survey of Path Planning Algorithms for Mobile Robots

Abstract: Path planning algorithms are used by mobile robots, unmanned aerial vehicles, and autonomous cars in order to identify safe, efficient, collision-free, and least-cost travel paths from an origin to a destination. Choosing an appropriate path planning algorithm helps to ensure safe and effective point-to-point navigation, and the optimal algorithm depends on the robot geometry as well as the computing constraints, including static/holonomic and dynamic/non-holonomically-constrained systems, and requires a comprehensive understanding of contemporary solutions. The goal of this paper is to help novice practitioners gain an awareness of the classes of path planning algorithms used today and to understand their potential use cases—particularly within automated or unmanned systems. To that end, we provide broad, rather than deep, coverage of key and foundational algorithms, with popular algorithms and variants considered in the context of different robotic systems. The definitions, summaries, and comparisons are relevant to novice robotics engineers and embedded system developers seeking a primer of available algorithms.