Micro air vehicle path planning in fuzzy quadtree framework

Fuzzy quadtree framework has been utilized to develop a path planner for a fixed wing micro air vehicle (MAV). The fuzzy quadtree being computationally efficient can efficiently meet the computational requirements of a micro air vehicle, and therefore, does not require high capacity processor onboard. The proposed algorithm can provide optimal and safe path because it can avoid a pop up obstacle in real time while significantly reducing both the space and the time complexity. Some issues which are very pertinent to the MAV path planning like vehicle dimensions and safety measures for congested environment have been taken into account in the code developed. Besides, during the quadtree generation the constraint of turn rate kinematics of the MAV has been included.

[1]  Micha Sharir,et al.  Planning a purely translational motion for a convex object in two-dimensional space using generalized Voronoi diagrams , 2016, Discret. Comput. Geom..

[2]  Jacky Baltes,et al.  Fuzzy Potential Energy for a Map Approach to Robot navigation , 2006, 2006 IEEE International Conference on Fuzzy Systems.

[3]  Yoshiaki Kuwata,et al.  Real-time Trajectory Design for Unmanned Aerial Vehicles using Receding Horizon Control , 2003 .

[4]  Kimon P. Valavanis,et al.  Evolutionary algorithm based offline/online path planner for UAV navigation , 2003, IEEE Trans. Syst. Man Cybern. Part B.

[5]  Jozef Vörös.  Low-cost implementation of distance maps for path planning using matrix quadtrees and octrees , 2001 .

[6]  Michisuke Jo,et al.  Path Search by Means of Quadtree, Octree and Fast Fourier Transform. , 1996 .

[7]  Claudio De Persis,et al.  Proceedings of the 41st IEEE Conference on Decision and Control , 2002 .

[8]  Rodney A. Brooks,et al.  Solving the Find-Path Problem by Good Representation of Free Space , 1983, Autonomous Robot Vehicles.

[9]  Enrico Pagello,et al.  A New Cell-subdivision Approach to Plan Free Translations in Cluttered Environments , 2003, J. Intell. Robotic Syst..

[10]  Ilya V. Kolmanovsky,et al.  Predictive energy management of a power-split hybrid electric vehicle , 2009, 2009 American Control Conference.

[11]  Irene Gargantini,et al.  An effective way to represent quadtrees , 1982, CACM.

[12]  Oussama Khatib,et al.  Real-Time Obstacle Avoidance for Manipulators and Mobile Robots , 1986 .

[13]  Tomás Lozano-Pérez,et al.  An algorithm for planning collision-free paths among polyhedral obstacles , 1979, CACM.

[14]  Sanjiv Singh,et al.  An efficient on-line path planner for outdoor mobile robots , 2000, Robotics Auton. Syst..

[15]  Jozef Vörös.  A strategy for repetitive neighbor finding in images represented by quadtrees , 1997, Pattern Recognit. Lett..

[16]  Vikram Kapila,et al.  Optimal path planning for unmanned air vehicles with kinematic and tactical constraints , 2002, Proceedings of the 41st IEEE Conference on Decision and Control, 2002..

[17]  Danny Ziyi Chen,et al.  A framed-quadtree approach for determining Euclidean shortest paths in a 2-D environment , 1997, IEEE Trans. Robotics Autom..

[18]  Kuo-Yang Tu.  Fuzzy Potential Energy for a Map Approach to Robot navigation , 2006, 2006 IEEE International Conference on Fuzzy Systems.

[19]  Danny Ziyi Chen,et al.  Planning Shortest Paths among 2D and 3D Weighted Regions Using Framed-Subspaces , 1998, Int. J. Robotics Res..

[20]  L. Singh,et al.  Trajectory generation for a UAV in urban terrain, using nonlinear MPC , 2001, Proceedings of the 2001 American Control Conference. (Cat. No.01CH37148).

[21]  Zhu Xiao-ping,et al.  A Feedback Guidance Strategy for an Autonomous Mini-air-vehicle , 2006 .