A vision-based auxiliary system of multirotor unmanned aerial vehicles for autonomous rendezvous and docking

Unmanned aerial vehicles (UAVs) are versatile in maneuverability for both civilian and military applications. To facilitate the long-term tasks of UAVs, autonomous rendezvous and docking (ARaD) will be a need in the emerging field of UAV research. In this paper, we proposed a vision-based auxiliary system (VAS) for multirotor UAVs to implement autonomous rendezvous and docking. The VAS consists of image acquisition and processing unit, wireless communication unit, and tracking and docking control unit. Continuously adaptive mean shift (CamShift) algorithm was applied for tracking the target and obtaining its 3D coordinates. A specific Zigbee protocol was designed to ensure the steady and rapid transmission of status data between UAVs and the ground station. A straight-forward tracking and docking control algorithm was proposed to assist the rendezvous and docking between the two UAVs. Physical simulation experiments were performed by two six-rotor rotorcrafts, which demonstrate the feasibility and practicability of our proposed vision-based auxiliary system for the future application.

[1]  Avishek Chakraborty,et al.  A distributed operating system supporting strong mobility of reconfigurable computing applications in a swarm of unpiloted airborne vehicles , 2009, 2009 International Conference on Field-Programmable Technology.

[2]  Marcello R. Napolitano,et al.  Modeling and control issues for autonomous aerial refueling for UAVs using a probe–drogue refueling system , 2004 .

[3]  Zhipu Jin,et al.  Scheduling and sequence reshuffle for autonomous aerial refueling of multiple UAVs , 2006, 2006 American Control Conference.

[4]  Yizong Cheng,et al.  Mean Shift, Mode Seeking, and Clustering , 1995, IEEE Trans. Pattern Anal. Mach. Intell..

[5]  Min Zhou,et al.  Analysis and design of ZigBee MAC layers protocol , 2010, 2010 International Conference on Future Information Technology and Management Engineering.

[6]  Wright-Patterson Afb,et al.  Automated Aerial Refueling: Extending the Effectiveness of Unmanned Air Vehicles , 2005 .

[7]  Pascual Campoy Cervera,et al.  A vision-based strategy for autonomous aerial refueling tasks , 2013, Robotics Auton. Syst..

[8]  Boris Pervan,et al.  Autonomous Airborne Refueling of Unmanned Air Vehicles Using the Global Positioning System , 2007 .

[9]  Delin Luo,et al.  A guidance law for UAV autonomous aerial refueling based on the iterative computation method , 2014 .

[10]  Bo Yang,et al.  Target tracking using predicted Camshift , 2008, 2008 7th World Congress on Intelligent Control and Automation.

[11]  Monish D. Tandale,et al.  Vision-Based Sensor and Navigation System for Autonomous Air Refueling , 2005 .

[12]  Eunmi Oh,et al.  An Integral Framework of Task Assignment and Path Planning for Multiple Unmanned Aerial Vehicles in Dynamic Environments , 2013, J. Intell. Robotic Syst..

[13]  Jeremiah Neubert,et al.  Vision Based Neuro-Fuzzy Controller for a Two Axes Gimbal System with Small UAV , 2013, Journal of Intelligent & Robotic Systems.

[14]  Kaarthik Sundar,et al.  Algorithms for Routing an Unmanned Aerial Vehicle in the Presence of Refueling Depots , 2013, IEEE Transactions on Automation Science and Engineering.

[15]  M. L. Fravolini,et al.  Simulation Environment for Machine Vision Based Aerial Refueling for UAVs , 2009, IEEE Transactions on Aerospace and Electronic Systems.

[16]  F. Saghafi,et al.  Vision-Based Trajectory Tracking Controller for Autonomous Close Proximity Operations , 2008, 2008 IEEE Aerospace Conference.

[17]  Gary R. Bradski,et al.  Real time face and object tracking as a component of a perceptual user interface , 1998, Proceedings Fourth IEEE Workshop on Applications of Computer Vision. WACV'98 (Cat. No.98EX201).

[18]  Mario G. Perhinschi,et al.  Machine Vision/GPS Integration Using EKF for the UAV Aerial Refueling Problem , 2008, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[19]  ChengYizong.  Mean Shift, Mode Seeking, and Clustering , 1995 .

[20]  Fariborz Saghafi,et al.  Vision-Based Navigation in Autonomous Close Proximity Operations using Neural Networks , 2011, IEEE Transactions on Aerospace and Electronic Systems.

[21]  Larry D. Hostetler,et al.  The estimation of the gradient of a density function, with applications in pattern recognition , 1975, IEEE Trans. Inf. Theory.

[22]  Zhang Xinguo,et al.  The UAV autonomous aerial refueling controller based on predictor auto disturbances rejection method , 2012 .

[23]  M. Innocenti,et al.  Vision-Based Autonomous Probe and Drogue Aerial Refueling , 2006, 2006 14th Mediterranean Conference on Control and Automation.

[24]  Ken Chen,et al.  A Meanshift-based imbedded computer vision system design for real-time target tracking , 2012, 2012 7th International Conference on Computer Science & Education (ICCSE).

[25]  Haibin Duan,et al.  Visual Measurement in Simulation Environment for Vision-Based UAV Autonomous Aerial Refueling , 2015, IEEE Transactions on Instrumentation and Measurement.

[26]  John Valasek,et al.  Vision based controller for autonomous aerial refueling , 2002, Proceedings of the International Conference on Control Applications.