Target tracking control and semi-physical simulation of Qball-X4 quad-rotor unmanned aerial vehicle

In this article, a set of integrated ground target tracking flight system has been proposed based on the Qball-X4 quad-rotor unmanned aerial vehicle hardware platform and the QuaRC software platform. Both of the hardware and software platforms are developed by Quanser Company, Canada. The proposed tracking and positioning algorithm could be divided into several stages. First, a tracker is developed based on an optical flow method to track the target; and then, in order to improve the reliability of tracking algorithm and also help in retrieving the lost target, a cascade target detector is developed; meanwhile, a model updated scheme aiming at some possible errors in tracking and detecting process is presented based on Positive-Negative (P-N) learning system; at last, a monocular visual positioning system is designed based on the corresponding navigation message. In addition, the effectiveness of the proposed flight control system is verified by both simulation and hardware-in-loop system results in several tracking flight tests.

[1]  Vincent Lepetit,et al.  Keypoint recognition using randomized trees , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[2]  Zhiwei Guan,et al.  A Multi-Objective Optimization Model for Planning Unmanned Aerial Vehicle Cruise Route , 2016 .

[3]  James M. Conrad,et al.  A survey of quadrotor Unmanned Aerial Vehicles , 2012, 2012 Proceedings of IEEE Southeastcon.

[4]  Lingling Sun,et al.  The design of quad-rotor environmental monitoring system based on Internet of Things , 2015, 2015 IEEE 16th International Conference on Communication Technology (ICCT).

[5]  野波 健蔵,et al.  Autonomous flying robots : unmanned aerial vehicles and micro aerial vehicles , 2010 .

[6]  Alonzo Kelly,et al.  Toward Reliable Off Road Autonomous Vehicles Operating in Challenging Environments , 2006, Int. J. Robotics Res..

[7]  Yangquan Chen,et al.  Using a multispectral autonomous unmanned aerial remote sensing platform (AggieAir) for riparian and wetlands applications , 2011, 2011 IEEE International Geoscience and Remote Sensing Symposium.

[8]  YangQuan Chen,et al.  Autopilots for small unmanned aerial vehicles: A survey , 2010 .

[9]  Takeo Kanade,et al.  Vision-Based Autonomous Helicopter Research at Carnegie Mellon Robotics Institute 1991-1997 , 1998 .

[10]  YangQuan Chen,et al.  Survey of thermal infrared remote sensing for Unmanned Aerial Systems , 2014, 2014 International Conference on Unmanned Aircraft Systems (ICUAS).

[11]  Takeo Kanade,et al.  An Iterative Image Registration Technique with an Application to Stereo Vision , 1981, IJCAI.

[12]  Gaurav S. Sukhatme,et al.  Landing a Helicopter on a Moving Target , 2007, Proceedings 2007 IEEE International Conference on Robotics and Automation.

[13]  Han Xia Study of OpenCV Applied in Intelligent Video Surveillance , 2009 .

[14]  Jiri Matas,et al.  P-N learning: Bootstrapping binary classifiers by structural constraints , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[15]  Stephen Cameron,et al.  OATS: Oxford Aerial Tracking System , 2007, Robotics Auton. Syst..

[16]  Glen Bright,et al.  Quad-Rotor Unmanned Aerial Vehicle Helicopter Modelling & Control , 2011 .

[17]  Kenzo Nonami,et al.  Autonomous Flying Robots , 2010 .

[18]  郑云龙 Unmanned aerial vehicle , 2015 .

[19]  Yangquan Chen,et al.  AggieAir — a low-cost autonomous multispectral remote sensing platform: New developments and applications , 2009, IEEE International Geoscience and Remote Sensing Symposium.

[20]  Omead Amidi,et al.  An autonomous vision-guided helicopter , 1996 .

[21]  Janne Heikkilä,et al.  A four-step camera calibration procedure with implicit image correction , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[22]  Kevin L. Moore,et al.  High-Order and Model Reference Consensus Algorithms in Cooperative Control of MultiVehicle Systems , 2007 .

[23]  Lu Liu,et al.  Optimal PID controller design with Kalman filter for Qball-X4 quad-rotor unmanned aerial vehicle , 2017 .