UAV feasible path planning based on disturbed fluid and trajectory propagation

Abstract In this paper, a novel algorithm based on disturbed fluid and trajectory propagation is developed to solve the three-dimensional (3-D) path planning problem of unmanned aerial vehicle (UAV) in static environment. Firstly, inspired by the phenomenon of streamlines avoiding obstacles, the algorithm based on disturbed fluid is developed and broadened. The effect of obstacles on original fluid field is quantified by the perturbation matrix, where the tangential matrix is first introduced. By modifying the original flow field, the modified one is then obtained, where the streamlines can be regarded as planned paths. And the path proves to avoid all obstacles smoothly and swiftly, follow the shape of obstacles effectively and reach the destination eventually. Then, by considering the kinematics and dynamics equations of UAV, the method called trajectory propagation is adopted to judge the feasibility of the path. If the planned path is unfeasible, repulsive and tangential parameters in the perturbation matrix will be adjusted adaptively based on the resolved state variables of UAV. In most cases, a flyable path can be obtained eventually. Simulation results demonstrate the effectiveness of this method.

[1]  Jing Chen,et al.  Real-time trajectory planning for UCAV air-to-surface attack using inverse dynamics optimization method and receding horizon control , 2013 .

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

[3]  Mingyue Ding,et al.  Route Planning for Unmanned Aerial Vehicle (UAV) on the Sea Using Hybrid Differential Evolution and Quantum-Behaved Particle Swarm Optimization , 2013, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[4]  Wang Hong-lu.  Design of Landing Trajectory and Safe Interface Area for RLV with Initial Interface Errors , 2014 .

[5]  Y. Volkan Pehlivanoglu,et al.  A new vibrational genetic algorithm enhanced with a Voronoi diagram for path planning of autonomous UAV , 2012 .

[6]  Mingyue Ding,et al.  Phase Angle-Encoded and Quantum-Behaved Particle Swarm Optimization Applied to Three-Dimensional Route Planning for UAV , 2012, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.

[7]  Stefan Hrabar,et al.  3D path planning and stereo-based obstacle avoidance for rotorcraft UAVs , 2008, 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[8]  Oussama Khatib,et al.  Real-Time Obstacle Avoidance for Manipulators and Mobile Robots , 1985, Autonomous Robot Vehicles.

[9]  Sylvain Miossec,et al.  Fast C1 proximity queries using support mapping of sphere-torus-patches bounding volumes , 2009, 2009 IEEE International Conference on Robotics and Automation.

[10]  James J. Kuffner,et al.  Multipartite RRTs for Rapid Replanning in Dynamic Environments , 2007, Proceedings 2007 IEEE International Conference on Robotics and Automation.

[11]  Mou Chen,et al.  A modified ant optimization algorithm for path planning of UCAV , 2008, Appl. Soft Comput..

[12]  René van Paassen,et al.  Artificial Force Field for Haptic Feedback in UAV Teleoperation , 2009, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.

[13]  Honglun Wang,et al.  Three-dimensional Path Planning for Unmanned Aerial Vehicle Based on Interfered Fluid Dynamical System , 2015 .

[14]  C. Zong-ji,et al.  Study on UAV Path Planning Approach Based on Fuzzy Virtual Force , 2010 .

[15]  Haibin Duan,et al.  An improved constrained differential evolution algorithm for unmanned aerial vehicle global route planning , 2015, Appl. Soft Comput..

[16]  Mohammad A. Jaradat,et al.  Autonomous mobile robot dynamic motion planning using hybrid fuzzy potential field , 2012, Soft Comput..

[17]  Han Tong,et al.  Path Planning of UAV Based on Voronoi Diagram and DPSO , 2012 .

[18]  Vincent Roberge,et al.  Comparison of Parallel Genetic Algorithm and Particle Swarm Optimization for Real-Time UAV Path Planning , 2013, IEEE Transactions on Industrial Informatics.

[19]  Igor Fernández de Bustos,et al.  A new APF strategy for path planning in environments with obstacles , 2005 .

[20]  Dawei Li,et al.  Three-dimensional path planning for unmanned aerial vehicles based on fluid flow , 2014, 2014 IEEE Aerospace Conference.

[21]  Ozgur Koray Sahingoz,et al.  Optimal UAV path planning in a 3D threat environment by using parallel evolutionary algorithms , 2013, 2013 International Conference on Unmanned Aircraft Systems (ICUAS).

[22]  Zhi-Hong Guan,et al.  Managing group behaviors in swarm systems by associations , 2006, 2006 American Control Conference.

[23]  Stephen Waydo,et al.  Using Stream Functions for Complex Behavior and Path Generation , 2003 .

[24]  Asokan Thondiyath,et al.  Multipoint potential field method for path planning of autonomous underwater vehicles in 3D space , 2013, Intell. Serv. Robotics.

[25]  L. Blasi,et al.  Smooth Flight Trajectory Planning in the Presence of No-Fly Zones and Obstacles , 2010 .

[26]  Seid H. Pourtakdoust,et al.  Optimal maneuver-based motion planning over terrain and threats using a dynamic hybrid PSO algorithm , 2013 .

[27]  Steven M. LaValle,et al.  Time-optimal paths for a Dubins airplane , 2007, 2007 46th IEEE Conference on Decision and Control.

[28]  Jiang Chang-sheng,et al.  Short communication: A modified ant optimization algorithm for path planning of UCAV , 2008 .

[29]  Zhang Chun.  Mobile Robot Path Planning Based on Rolling Windows , 2002 .

[30]  Lv Wentao.  Three-dimensional Path Planning for Unmanned Aerial Vehicles Based on Principles of Stream Avoiding Obstacles , 2013 .

[31]  Aude Billard,et al.  A dynamical system approach to realtime obstacle avoidance , 2012, Autonomous Robots.

[32]  Honglun Wang,et al.  3-D dynamic path planning for UAV based on interfered fluid flow , 2014, Proceedings of 2014 IEEE Chinese Guidance, Navigation and Control Conference.

[33]  Jizhong Xiao,et al.  A literature review of UAV 3D path planning , 2014, Proceeding of the 11th World Congress on Intelligent Control and Automation.