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基于NURBS和GOBL-ACDE的航迹规划算法 被引量:7

Path planning algorithm based on NURBS and GOBL-ACDE
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摘要 针对复杂地形条件下无人机低空突防动态航迹规划实时性及精确性的问题,提出了基于广义反向学习的自适应约束差分进化(generalized opposition-based learning adaptive constrained differential evolution,GOBL-ACDE)算法,结合非均匀有理B样条(non-uniform rational B-spline,NURBS)平滑策略,提高了多威胁复杂地形下动态航迹规划的精确性、高效性及适航性。首先,构建航迹规划任务模型,建立目标代价及约束限制函数,提出一种高度转换方法,有效提高低空突防能力;其次,将NURBS平滑策略与B样条插值以及贝塞尔曲线对比分析;再次,应用广义反向学习、自适应排序变异及自适应权衡模型,改善约束条件下算法动态性、收敛性及寻优性能;最后,通过静态与动态环境对比仿真试验,验证了所提方法在多威胁复杂地形下寻优精度高、鲁棒性强、动态性好以及可靠性优的特点,能够规划出精确、高效、适航的低空突防航迹。 To satisfy the requirements of instantaneity and accuracy of unmanned aerial vehicle(UAV)dynamic path planning for low-altitude penetration under complex terrain conditions,a generalized opposition-based learning adaptive constrained differential evolution(GOBL-ACDE)algorithm is proposed,which is combined with the non-uniform rational B-spline(NURBS)smoothing strategy to improve the accuracy,efficiency,feasibility and airworthiness of UAV dynamic path planning.Firstly,the model of the planning task is constructed as well as the objective cost and constraint function,and we propose a height conversion method to effectively improve the low altitude penetration ability.Then,the performance of the NURBS smoothing strategy is compared with B-spline interpolation and the Bezier curve.In addition,the diversity,convergence and optimization performance of differential evolution are improved through introducing generalized opposition-based learning,adaptive ranking mutation operators and adaptive trade-off model into the algorithm.Finally,through the comparative simulation in static and dynamic environments,it is shown that the proposed method has high accuracy,strong robustness,terrific dynamic performance and excellent reliability in the multi-threat complex terrain,and is able to plan an accurate,efficient and feasible low-altitude penetration path for UAV.
作者 吴文海 郭晓峰 周思羽 WU Wenhai;GUO Xiaofeng;ZHOU Siyu(Department of Aviation Control and Command, Qingdao Campus, Naval Aviation University, Qingdao 266041, China)
出处 《系统工程与电子技术》 EI CSCD 北大核心 2020年第5期1073-1082,共10页 Systems Engineering and Electronics
基金 国家重点研发计划(2018YFC0806900,2016YFC0800606,2016YFC0800310)资助课题。
关键词 动态规划 差分进化 非均匀有理B样条 威胁回避 低空突防 dynamic planning differential evolution non-uniform rational B-spline(NURBS) threat avoidance low-altitude penetration
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  • 1雍恩米,陈磊,唐国金.飞行器轨迹优化数值方法综述[J].宇航学报,2008,29(2):397-406. 被引量:131
  • 2DUAN HaiBin 1 ,SHAO Shan 2 ,SU BingWei 3 &ZHANG Lei 41 State Key Laboratory of Science and Technology on Holistic Flight Control,School of Automation Science and Electrical Engineering, Beijing University of Aeronautics and Astronautics,Beijing 100191,China,2 Flight Control Department,Shenyang Aircraft Design and Research Institute,Shenyang 110035,China,3 Beijing Institute of Near Space Vehicle’s System Engineering,Beijing 100076,China,4Integration and Project Section,Air Force Equipment Academy,Beijing 100085,China.New development thoughts on the bio-inspired intelligence based control for unmanned combat aerial vehicle[J].Science China(Technological Sciences),2010,53(8):2025-2031. 被引量:34
  • 3高晓光,符小卫,宋绍梅.多UCAV航迹规划研究[J].系统工程理论与实践,2004,24(5):140-143. 被引量:25
  • 4赵红,何华灿,赵宗涛,虞蕾.一种地形分析方法在航迹规划中的应用[J].空军工程大学学报(自然科学版),2006,7(4):36-38. 被引量:3
  • 5高国华.大范围多路径规划问题研究[D].长沙:国防科技大学,1999.
  • 6潘亮.复杂环境下多目标任务规划方法及实现技术研究[D].长沙:国防科技大学机电工程与自动化学院,2003.
  • 7严平.无人飞行器航迹规划与任务分配方法研究[D].武汉:华中科技大学图像识别与人工智能研究所,2006.
  • 8Bortoff S A.Path planning for UAVs[C]//Proceedings of the 2000 American Control Conference,2000,1(6): 364-368.
  • 9Beard R W,McLain T W,Goodrich M,et al.Coordinated target assignment and intercept for unmanned air vehicles[J].IEEE Transactions on Robotics and Automation,2002,18(6): 911-922.
  • 10Bellingham J,Tillerson M,Richards A,et al.Multi-task allocation and path planning for cooperating UAVs[M]//Cooperative Control: Models,Applications and Algorithms.Dordrecht: Kluwer Academic Publishers,2003: 23-41.

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