Acta Geodaetica et Cartographica Sinica >

2015 , Vol. 44 >Issue 4: 462 - 470

DOI: https://doi.org/10.11947/j.AGCS.2015.20120523

Quickly Planning TF/TA2 Trajectory by Artificial Immune Algorithm

  • LIU Lifeng ,
  • YANG Fei ,
  • ZHANG Shuqing ,
  • KONG Weihua ,
  • WANG Yinxing
Expand
  • 1. Shandong University of Technology, Zibo 255049, China;
    2. Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China;
    3. Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China

Received date: 2012-09-05

  Revised date: 2014-09-07

  Online published: 2015-04-27

Supported by

The Key Research Program of the Chinese Academy of Sciences (No. KZZD-EW-07-02);The National Natural Science Foundation of China (No. 41301607);The National Natural Science Foundation of Shandong Province of China (No. ZR2012DL06)

Fold

Abstract

Flight path planning by artificial immune algorithm approach met the requirements of aircraft's flyability and operation is proposed for the problem of single and double TF/TA2 flight path planning. Punishment function (affinity function) with comprehensive 3D threat information is designed. A comprehensive threat model is formed including dynamic and static threats and no-fly-zone. Accordingly, single and dual flight paths are planned by AIA, which have been compared with the paths by GA. The results show that, GA's planned a quick and longer path compared under simple threat environment; in complex environments, GA has high failure rate (greater than 95%) for single aircraft, but it is failed for double aircrafts. For the single and double aircrafts, AIA can provides one optimal and more candidate optimal flight paths.

Key words： path planning; artificial immune algorithm; GA; TF/TA2

Cite this article

LIU Lifeng , YANG Fei , ZHANG Shuqing , KONG Weihua , WANG Yinxing . Quickly Planning TF/TA2 Trajectory by Artificial Immune Algorithm[J]. Acta Geodaetica et Cartographica Sinica, 2015 , 44(4) : 462 -470 . DOI: 10.11947/j.AGCS.2015.20120523

References

[1] TANG Q, ZHANG X G, LIU X C. TF/TA2 Trajectory Tracking Using Nonlinear Predictive Control Approach[J]. Journal of Systems Engineering and Electronics, 2006, 17(2): 396-401.
[2] LI Linyi, LI Deren. Image Texture Classification Based on Immune Particle Swarm Optimization[J]. Acta Geodaetica et Cartographica Sinica, 2008(5), 37(2): 185-195. (李林宜, 李德仁. 基于免疫粒子群优化算法的影像纹理分类[J]. 测绘学报, 2008(5), 37(2): 185-195.)
[3] ALLAIRE F C J, TARBOUCHI M, LABONTÉ G, et al. FPGA Implementation of Genetic Algorithm for UAV Real-time Path Planning[J]. Journal of Intelligent and Robotic Systems, 2009, 54(1-3): 495-510.
[4] FOO J L, KNUTZON J, KALIVARAPU V, et al. Path Planning of Unmanned Aerial Vehicles Using B-splines and Particle Swarm Optimization[J]. Journal of Aerospace Computing, Information, and Communication, 2009, 6: 271-290.
[5] SABO C, COHEN K, KUMAR M, et al. Effectiveness of 2D Path Planning in Real Time Using Fuzzy Logic[C]//Proceedings of the 48th AIAA Aerospace Sciences Meeting Including the New Horizons Forum and Aerospace Exposition. Orlando: AIAA, 2010: 1-13.
[6] WARREN C W. Fast Path Planning Using Modified A* Method[J]. Robotics and Automation, 1993, 2: 662-667.
[7] HAMMOURI O M, MATALGAH M M. Voronoi Path Planning Technique for Recovering Communication in UAVs[C]//Proceedings of ACS International Conference on Computer Systems and Applications. Doha: ACS, 2008: 403-406.
[8] ZHANG Keshi, WANG Zhengping. On Optimizing Large-scale Air-combat Formation with Simulated: Annealing GA (Genetic Algorithm)[J]. Journal of North Western Polytechnical University, 2003, 21(4): 477-480. (张科施, 王正平. 基于遗传模拟退火算法的空战编队优化研究[J]. 西北工业大学学报, 2003, 21(4): 477-480.)
[9] QIU Z P, ZHANG Y. Parametric Optimization Design of Aircraft Based on Hybrid Parallel Multi-objective Tabu Search Algorithm[J]. Chinese Journal of Aeronautics, 2010, 23 (4): 430-437.
[10] BAI Zhipeng, CHEN Fuji. Compound Method of Taboo Search and Genetic Algorithm to Sove Knapsack Problem[J]. Automation & Information Engineering, 2007, 28(2): 9-11. (白志鹏, 陈福集. 禁忌搜索与 GA 算法结合求解背包问题[J]. 自动化与信息工程, 2007, 28(2): 9-11.)
[11] ZHANG Fuwei, LI Jun, MENG Pinchao, et al. Survey of Multi-objective Evolutionary Algorithms[J]. Journal of Changchun University of Science and Technology: Natural Science Edition, 2012, 35(3): 102-105. (张福威,李军,孟品超, 等. 多目标进化算法综述[J]. 长春理工大学学报: 自然科学版, 2012, 35(3): 102-105.)
[12] PONGPUNWATTANA A, RYSDYK R. Evolution-based Dynamic Path Planning for Autonomous Vehicles[J]. Studies in Computational Intelligence, 2007, 70: 113-145.
[13] DONG Z, YUAN J. A Formulation for Collision Identification and Distance Calculation in Motion Planning Using Neural Networks[J]. The International Journal of Advanced Manufacturing Technology, 1993, 8(4): 227-234.
[14] YOU Shucheng, YAN Tailai. A Study on Artificial Neural Net Work Based Surface Interpolation[J]. Acta Geodaetica et Cartographica Sinica, 2000, 29(1): 30-34. (尤淑撑, 严泰来. 基于人工神经网络面插值的方法研究[J]. 测绘学报, 2000, 29(1): 30-34.)
[15] LI Lin. Variable Query Algebra and Shortest Path Analysis[J]. Acta Geodaetica et Cartographica Sinica, 2000, 29(1): 59-63. (李霖. 变量查询代数及最短路径分析[J]. 测绘学报, 2000, 29(1): 59-63.)
[16] TANG Luliang, CHANG Xiaomeng, LI Qingquan. The Knowledge Modeling and Route Planning Based on Taxi' Experience[J]. Acta Geodaetica et Cartographica Sinica, 2010, 39(4): 404-409. (唐炉亮,常晓猛,李清泉. 出租车经验知识建模与路径规划算法[J]. 测绘学报, 2010(8), 39(4): 404-409.)
[17] ZHENG Nianbo, LU Feng, LI Qingquan, et al. The Adaption of A* Algorithm for Least-time Path in Time-dependent Transportation Networks with Turn Delays[J]. Acta Geodaetica et Cartographica Sinica, 2010, 39(5): 404-409. (郑年波, 陆锋, 李清泉, 等. 顾及转向延误的时间依赖A*最短路径算法[J]. 测绘学报, 2010, 39(5): 404-409.)
[18] JIANG Y, WANG H G, FANG L J, et al. 2006. Motion Planning for Climbing Robot Based on Hybrid Navigation[J]. Advance in Machine Learning and Computing, 2006, 3930: 91-100.
[19] KURNAZ S, KAYNAK O, KONAKO L U E. Adaptive Neuro-fuzzy Inference System Based Autonomous Flight Control of Unmanned Air Vehicles[J]. Expert Systems with Applications, 2007, 37(2): 14-21.
[20] XIN Y, ZHU Q D, YAN Y J. Collision Avoidance Planning in Multi-robot System Based on Improved Artificial Potential Field and Rules[J]. Journal of Harbin Institute of Technology, 2009, 16(3): 413-418.
[21] LIU Hanli, ZHOU Chenghu, ZHU Axin, et al. Multi-Population Genetic Neural Network Model for Short-term Traffic Flow Prediction at Intersections[J]. Acta Geodaetica et Cartographica Sinica, 2009, 38(4): 363-368. (刘汉丽, 周成虎, 朱阿兴, 等. 多子群遗传神经网络模型用于路口短时交通流量预测[J]. 测绘学报, 2009, 38(4): 363-368.)
[22] ZHAI Renjian, WU Fang, DENG Hongyan, et al. An Automated Selection Model of Ditch Based on Multi-objective Optimization by Genetic Algorithm[J]. Acta Geodaetica et Cartographica Sinica, 2008, 37(1): 108-113. (翟仁健, 武芳, 邓红艳, 等. 基于遗传多目标优化的人工河网自动选取模型[J]. 测绘学报, 2008, 37(1): 108-113.)
[23] MAYORGA R V, WONG A K C. A Robust Method for the Concurrent Motion Planning of Multi-manipulators Systems[J]. Journal of Intelligent and Robotic Systems, 1997, 19(1): 73-88.
[24] YU Z G, SONG S M, DUAN G R. A New Artificial Immune Algorithm and Its Application for Optimization Problems[J]. Journal of Harbin Institute of Technology, 2006, 13(2), 129-133.
[25] BHADURI A. University Time Table Scheduling Using Genetic Artificial Immune Network[C]//International Conference on Advances in Recent Technologies in Communication and Computing. Los Alamos: IEEE Computer Society, 2009: 289-292.
[26] MALIM M R, KHADER A T, MUSTAFA A. Artificial Immune Algorithms for University Timetabling[C]//Proceedings of the 6th International Conference on Practice and Theory of Automated Timetabling. Brno:[s. n.], 2006: 234-245.
[27] OBERHEID H, SÖFFKER D. Cooperative Arrival Management in Air Traffic Control: A Coloured Petri Net Model of Sequence Planning[J]. Applications and Theory of Petri Nets, 2008, 5062: 348-367.
Options
Outlines

/

〈 〉