Abstract:The existing route planning methods can not meet the optimal path and real-time requirements simultaneously. A method based on cultural algorithm is proposed to solve the problem of unmanned aerial vehicle(UAV) online path planning. According to the characteristics of cultural algorithm, online route planning method is combined with offline route planning method and they are fused into the population space of cultural algorithm. By extracting the knowledge, the situation knowledge is extracted from the initial path information, and the normative knowledge is recovered from the variation ranges of nodes. The planning space is limited by the knowledge and the time of planning is reduced. Different methods are combined to remedy the deficiencies of the existing methods. The experiment shows the proposed method searches target effectively in complex dynamic environments, and its planning speed is higher than that of other online route planning algorithms. Moreover, it can satisfy the real-time requirement, plan the shorter path and shorten the aircraft mission time.
[1] GOERZEN C, KONG Z D, METTLER B. A Survey of Motion Planning Algorithms from the Perspective of Autonomous UAV Guidance. Journal of Intelligent and Robotic Systems, 2010, 57: 65-100. [2] PEHLIVANOGLU Y V. A New Vibrational Genetic Algorithm Enhanced with a Voronoi Diagram for Path Planning of Autonomous UAV. Aerospace Science and Technology, 2012, 16(1): 47-55. [3] HUPTYCH M, RCK S. Online Path Planning in Dynamic Environments Using the Curve Shortening Flow Method. Production Engineering, 2015, 9(5): 613-621. [4] WANG H J, XIONG W. Research on Global Path Planning Based on Ant Colony Optimization for AUV. Journal of Marine Science and Application, 2009, 8(1): 58-64. [5] CASTILLO O, TRUJILLO L, MELIN P. Multiple Objective Genetic Algorithms for Path-Planning Optimization in Autonomous Mobile Robots. Soft Computing, 2007, 11(3): 269-279. [6] SHUM A, MORRIS K, KHAJEPOUR A. Direction-Dependent Optimal Path Planning for Autonomous Vehicles. Robotics & Auto-nomous Systems, 2015, 70: 202-214. [7] LI P, DUAN H B. Path Planning of Unmanned Aerial Vehicle Based on Improved Gravitational Search Algorithm. Science China Technological Sciences, 2012, 55(10): 2712-2719. [8] LIU X M, YUAN J, WANG K S. A Problem-Specific Genetic Algorithm for Path Planning of Mobile Robot in Greenhouse // WANG K S, KOVACS G L, WOZN Y M, et al., eds. Knowledge Enterprise: Intelligent Strategies in Product Design, Manufacturing, and Ma-nagement. New York, USA: Springer, 2006: 211-216. [9] WEN N F, SU X H, MA P J, et al. Online UAV Path Planning in Uncertain and Hostile Environments. International Journal of Machine Learning and Cybernetics, 2015. DOI: 10.1007/s13042-015-0339-4. [10] CHEN Y B, YU J Q, MEI Y S, et al. Modified Central Force Optimization(MCFO) Algorithm for 3D UAV Path Planning. Neurocomputing, 2016, 171: 878-888. [11] MOON S, OH E, SHIM D H. An Integral Framework of Task Assignment and Path Planning for Multiple Unmanned Aerial Vehicles in Dynamic Environments. Journal of Intelligent and Robotic Systems, 2013, 70(1): 303-313. [12] AL-MUTIB K, ALSULAIMAN M, EMADUDDIN M, et al. D* Lite Based Real-Time Multi-agent Path Planning in Dynamic Environments // Proc of the 3rd International Conference on Computational Intelligence, Modelling & Simulation. Washington, USA: IEEE, 2011: 170-174. [13] SARANYA C, UNNIKRISHNAN M, ALI S A, et al. Terrain Based D* Algorithm for Path Planning. IFAC-PapersOnLine, 2016, 49(1): 178-182. [14] QU H, XING K, ALEXANDER T. An Improved Genetic Algorithm with Co-evolutionary Strategy for Global Path Planning of Multiple Mobile Robots. Neurocomputing, 2013, 120: 509-517. [15] REYNOLDS R G, KINNAIRD-HEETHER L. Optimization Problem Solving with Auctions in Cultural Algorithms. Memetic Computing, 2013, 5(2): 83-94. [16] GUO Y N, CHENG J, CAO Y Y, et al. A Novel Multi-Population Cultural Algorithm Adopting Knowledge Migration. Soft Computing, 2011, 15(5): 897-905.