Unmanned Aerial Vehicle Flight Point Classification Algorithm Based on Symmetric Big Data

Unmanned aerial vehicles (UAVs) with auto-pilot capabilities are often used for surveillance and patrol. Pilots set the flight points on a map in order to navigate to the imaging point where surveillance or patrolling is required. However, there is the limit denoting the information such as absolute altitudes and angles. Therefore, it is required to set the information accurately. This paper hereby proposes a method to construct environmental symmetric big data using an unmanned aerial vehicle (UAV) during flight by designating the imaging and non-imaging points for surveillance and patrols. The K-Means-based algorithm proposed in this paper is then employed to divide the imaging points, which is set by the pilot, into K clusters, and K imaging points are determined using these clusters. Flight data are then used to set the points to which the UAV will fly. In our experiment, flight records were gathered through an UAV in order to monitor a stadium and the imaging and non-imaging points were set using the proposed method and compared with the points determined by a traditional K-Means algorithm. Through the proposed method, the cluster centroids and cumulative distance of its members were reduced by 87.57% more than with the traditional K-Means algorithm. With the traditional K-Means algorithm, imaging points were not created in the five points desired by the pilot, and two incorrect points were obtained. However, with the proposed method, two incorrect imaging points were obtained. Due to these two incorrect imaging points, the two points desired by the pilot were not generated.

[1]  Yunfeng Zhang,et al.  Mission planning of autonomous UAVs for urban surveillance with evolutionary algorithms , 2013, 2013 10th IEEE International Conference on Control and Automation (ICCA).

[2]  Jizhong Xiao,et al.  3D PRM based real-time path planning for UAV in complex environment , 2012, 2012 IEEE International Conference on Robotics and Biomimetics (ROBIO).

[3]  Monica N. Nicolescu,et al.  Natural methods for robot task learning: instructive demonstrations, generalization and practice , 2003, AAMAS '03.

[4]  Pedro Larrañaga,et al.  An empirical comparison of four initialization methods for the K-Means algorithm , 1999, Pattern Recognit. Lett..

[5]  Novel Certad,et al.  Trajectory Generation and Tracking Using the AR.Drone 2.0 Quadcopter UAV , 2015, 2015 12th Latin American Robotics Symposium and 2015 3rd Brazilian Symposium on Robotics (LARS-SBR).

[6]  Jongrae Kim,et al.  UAV path planning for maximum visibility of ground targets in an urban area , 2010, 2010 13th International Conference on Information Fusion.

[7]  Jong Hyuk Park,et al.  Graph-based motor primitive generation framework , 2015, Human-centric Computing and Information Sciences.

[8]  Jerry Y. H. Fuh,et al.  UAV surveillance mission planning with gimbaled sensors , 2014, 11th IEEE International Conference on Control & Automation (ICCA).

[9]  Paul Gerin Fahlstrom,et al.  Introduction to UAV Systems , 2012 .

[10]  Mario Sarcinelli-Filho,et al.  An automatic flight control system for the AR.Drone quadrotor in outdoor environments , 2015, 2015 Workshop on Research, Education and Development of Unmanned Aerial Systems (RED-UAS).

[11]  Sanghyuk Park,et al.  A Study on the Algorithm for Automatic Generation of Optimal Waypoint with Terrain Avoidance , 2009 .

[12]  Yunsick Sung,et al.  Structure Design of Surveillance Location-Based UAV Motor Primitives , 2016 .

[13]  Gianluca Dini,et al.  The verifier bee: A path planner for drone-based secure location verification , 2015, 2015 IEEE 16th International Symposium on A World of Wireless, Mobile and Multimedia Networks (WoWMoM).

[14]  Nicolas Petit,et al.  The Navigation and Control technology inside the AR.Drone micro UAV , 2011 .

[15]  Mario Sarcinelli-Filho,et al.  Outdoor waypoint navigation with the AR.Drone quadrotor , 2015, 2015 International Conference on Unmanned Aircraft Systems (ICUAS).

[16]  D. Alejo,et al.  Particle Swarm Optimization for collision-free 4D trajectory planning in Unmanned Aerial Vehicles , 2013, 2013 International Conference on Unmanned Aircraft Systems (ICUAS).