Volume 41 Issue 7
Jul.  2015
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LIANG Xiao, WANG Honglun, MENG Guanglei, et al. Path planning for UAV under three-dimensional real terrain in rescue mission[J]. Journal of Beijing University of Aeronautics and Astronautics, 2015, 41(7): 1183-1187. doi: 10.13700/j.bh.1001-5965.2014.0479(in Chinese)
Citation: LIANG Xiao, WANG Honglun, MENG Guanglei, et al. Path planning for UAV under three-dimensional real terrain in rescue mission[J]. Journal of Beijing University of Aeronautics and Astronautics, 2015, 41(7): 1183-1187. doi: 10.13700/j.bh.1001-5965.2014.0479(in Chinese)

Path planning for UAV under three-dimensional real terrain in rescue mission

doi: 10.13700/j.bh.1001-5965.2014.0479
  • Received Date: 30 Jul 2014
  • Rev Recd Date: 20 Nov 2014
  • Publish Date: 20 Jul 2015
  • Basing on the capability of three-dimensional flight and planning of optimal path, unmanned aerial vehicles (UAVs) can reach the disaster areas within shorter time than ground vehicles, which will improve the efficiency of rescue. Firstly, according to the real geographical environment, terrain is modeled by a mesh uniform method based on UAV constraints. Secondly, a data structure which is suitable for calculation is designed based on the characteristics of terrain data. Finally, the integrative performance function includes the deviation cost, height cost, terrain following/avoidance cost, threat cost and security distance cost. Both methods of waypoints cross and grid search instead of waypoints are engaged in the improved ant colony algorithm to make three-dimensional UAV path planning. The simulation results show that the method can deal with three-dimensional terrain data directly. While maintaining the topography of the premise, it can find the three-dimensional optimal path of UAV and improve the practical value of path planning technology.

     

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  • [1]
    Zhang X Y, Wu M, Peng J, et al.A rescue robot path planning based on ant colony optimization algorithm[C]//International Conference on Information Technology and Computer Science 2009.Piscataway, NJ: IEEE Press, 2009: 180-183.
    [2]
    Khanmohammadi S, Zarrin R S.Intelligent path planning for rescue robot[J].World Academy of Science, Engineering and Technology, 2011, 5(7): 607-612.
    [3]
    Norouzi M, Bruijn F D, Miro J V.Planning stable paths for urban search and rescue robots[J].Computer Science, 2012, 7416: 90-101.
    [4]
    Pang T, Ruan X G, Wang E S, et al.Search and rescue robot path planning in unknown environment[J].Applied Mechanics and Materials, 2013, 241: 1682-1687.
    [5]
    Pang T, Ruan X G, Wang E S, et al.Based on A* and Q-learning search and rescue robot navigation[J]. Telkomnika-Indonesian Journal of Electrical Engineering, 2012, 10(7): 1889-1896.
    [6]
    Sun H L, Yue L Y, Yao S Y.Study on selection of emergency rescue based on GIS[J].Advanced Materials Research, 2014, 864: 2804-2807.
    [7]
    Sullivan T A, Van J D.Multi-objective, multi-domain genetic optimization of a hydraulic rescue spreader[J].Mechanism and Machine Theory, 2014, 80: 35-51.
    [8]
    Liu T L, Wu C D, Li B, et al.The adaptive path planning research for a shape-shifting robot using particle swarm optimization[C]//International Conference on Natural Computation 2009.Piscataway, NJ: IEEE Press, 2009: 324-328.
    [9]
    洪晔, 房建成.基于HMDP的无人机三维路径规划[J].北京航空航天大学学报, 2009, 35(1): 100-103. Hong Y, Fang J C.Hierarchical Markov decision processes based path planning for UAV in three-dimensional environment[J].Journal of Beijing University of Aeronautics and Astronautics, 2009, 35(1): 100-103(in Chinese).
    [10]
    Adolf F M, Hirschmuller H.Meshing and simplification of high resolution urban surface data for UAV path planning[J].Journal of Intelligent and Robotic System, 2011, 61(1): 169-180.
    [11]
    Samar R, Rehman A.Autonomous terrain-following for unmanned air vehicles[J].Mechatronics, 2011, 21(5): 844-860.
    [12]
    Glabowski M, Musznicki B, Nowak P, et al.An algorithm for finding shortest path tree using ant colony optimization metaheuristic[J].Advances in Intelligent Systems and Computing, 2014, 233: 317-326.
    [13]
    Kolavali S R, Bhatnagar S.Ant colony optimization algorithms for shortest path problems[J].Computer Science, 2009, 5425: 37-44.
    [14]
    Roberge V, Tarbouchi M, Labonte G.Comparison of parallel genetic algorithm and particle swarm optimization for real-time UAV path planning[J].IEEE Transactions on Industrial Informatics, 2013, 9(1): 132-141.
    [15]
    Chen M, Wu Q X, Jiang C S.A modified ant optimization algorithm for path planning of UCAV[J].Applied Soft Computing, 2008, 8(4): 1712-1718.
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