第13卷第4期 智能系统学报 Vol.13 No.4 2018年8月 CAAI Transactions on Intelligent Systems Aug.2018 D0:10.11992/tis.201708031 网络出版地址：http:/kns.cnki.net/kcms/detail/23.1538.TP.20180402.1559.008.html 基于滚动时域的无人机动态航迹规划 王文彬2，秦小林2，张力戈2，张国华 (1.中国科学院成都计算机应用研究所，四川成都610041；2.中国科学院大学计算机与控制学院，北京 100080:3.广州大学智能软件研究院.广东广州510006) 摘要：针对带有动力学约束的多旋翼无人机航迹规划问题，提出了一种基于滚动时域控制和快速粒子群优 化(RHC-FPSO)方法。该方法引入了基于VORONOI图的代价图方法说明从航迹端点到达目标点的距离估 计。根据滚动时域和人工势场法的思想，将路径规划问题转化为优化问题，以最小距离和其他性能指标为代价 函数。设计评价函数准则，按照评价准则使用变权重粒子群优化算法求解。针对无人机靠近危险区飞行的问 题，将斥力场引入到代价函数中，提升其安全性。仿真实验结果显示，使用文中方法可以有效地在满足约束条 件下穿过障碍物区域，以及在复杂环境下可以动态计算。 关键词：航迹规划：滚动时域控制；VORONOI图；变权重：粒子群优化；人工势场 中图分类号：TP18:V279文献标志码：A文章编号：1673-4785(2018)04-0524-10 中文引用格式：王文彬，秦小林，张力戈，等.基于滚动时域的无人机动态航迹规划J智能系统学报，2018,13(4)：524-533. 英文引用格式：WANG Wenbin,QIN Xiaolin,ZHANG Lige,etal.Dynamic UAV trajectory planning based on receding horizon[Jl.. CAAI transactions on intelligent systems,2018,13(4):524-533. Dynamic UAV trajectory planning based on receding horizon WANG Wenbin'2,QIN Xiaolin"2,ZHANG Lige2,ZHANG Guohua (1.Chengdu Institute of Computer Applications,Chinese Academy of Sciences,Chengdu 610041,China;2.School of Computer and Control Engineering,University of Chinese Academy of Sciences,Beijing 100080,China;3.Academy of Intelligent Software, Guangzhou University,Guangzhou 510006,China) Abstract:Using receding horizon control and fast particle swarm optimization(RHC-FPSO),in this paper,we propose an algorithm for unmanned aerial vehicle(UAV)trajectory planning with dynamic constraints.We introduce the cost map method based on the VORONOI graph to estimate the distance from the end point of the trajectory to the target point.Using the concept of receding horizon control and the artificial potential field method,the path planning problem is transformed into an optimization problem,with the minimum distance and other performance indicators as cost func- tions.We design the evaluation function criteria based on the evaluation criteria and obtain the solution using a particle swarm optimization algorithm with variable weight.To address the problem in which a UAV approaches a danger zone, we introduce a repulsion field into the cost function to ensure safety.The simulation results show that the proposed method can effectively avoid obstacles within the constraint conditions and perform dynamic calculations in a complic- ated environment. Keywords:trajectory planning;receding horizon control;VORONOI graph;variable weight;particle swarm optimiza- tion;artificial potential field 无人机(UAV)航迹规划(trajectory planning) 环境信息的情况下获得性能最优的规划问题，是 是指在初始状态、任务目标、威胁区和一些已知 任务规划(mission planning)系统的关键技术之 一。其在任务规划系统中起着非常重要的作用， 收稿日期：2017-08-31.网络出版日期：2018-04-02 也是实现无人机智能导航并且完成任务的技术保 基金项目：国家自然科学基金项目(61402537). 通信作者：秦小林.E-mail:qinl@casit..ac.cn 障。基于飞行安全的需要，综合考虑障碍物、无DOI: 10.11992/tis.201708031 网络出版地址: http://kns.cnki.net/kcms/detail/23.1538.TP.20180402.1559.008.html 基于滚动时域的无人机动态航迹规划 王文彬1,2，秦小林1,2,3，张力戈1,2，张国华1,2 （1. 中国科学院 成都计算机应用研究所，四川 成都 610041; 2. 中国科学院大学 计算机与控制学院，北京 100080; 3. 广州大学 智能软件研究院，广东 广州 510006） 摘 要：针对带有动力学约束的多旋翼无人机航迹规划问题，提出了一种基于滚动时域控制和快速粒子群优 化 (RHC-FPSO) 方法。该方法引入了基于 VORONOI 图的代价图方法说明从航迹端点到达目标点的距离估 计。根据滚动时域和人工势场法的思想，将路径规划问题转化为优化问题，以最小距离和其他性能指标为代价 函数。设计评价函数准则，按照评价准则使用变权重粒子群优化算法求解。针对无人机靠近危险区飞行的问 题，将斥力场引入到代价函数中，提升其安全性。仿真实验结果显示，使用文中方法可以有效地在满足约束条 件下穿过障碍物区域，以及在复杂环境下可以动态计算。 关键词：航迹规划；滚动时域控制；VORONOI 图；变权重；粒子群优化；人工势场 中图分类号：TP18；V279 文献标志码：A 文章编号：1673−4785(2018)04−0524−10 中文引用格式：王文彬, 秦小林, 张力戈, 等. 基于滚动时域的无人机动态航迹规划[J]. 智能系统学报, 2018, 13(4): 524–533. 英文引用格式：WANG Wenbin, QIN Xiaolin, ZHANG Lige, et al. Dynamic UAV trajectory planning based on receding horizon[J]. CAAI transactions on intelligent systems, 2018, 13(4): 524–533. Dynamic UAV trajectory planning based on receding horizon WANG Wenbin1,2 ，QIN Xiaolin1,2,3 ，ZHANG Lige1,2 ，ZHANG Guohua1,2 (1. Chengdu Institute of Computer Applications, Chinese Academy of Sciences, Chengdu 610041, China; 2. School of Computer and Control Engineering, University of Chinese Academy of Sciences, Beijing 100080, China; 3. Academy of Intelligent Software, Guangzhou University, Guangzhou 510006, China) Abstract: Using receding horizon control and fast particle swarm optimization (RHC-FPSO), in this paper, we propose an algorithm for unmanned aerial vehicle (UAV) trajectory planning with dynamic constraints. We introduce the cost map method based on the VORONOI graph to estimate the distance from the end point of the trajectory to the target point. Using the concept of receding horizon control and the artificial potential field method, the path planning problem is transformed into an optimization problem, with the minimum distance and other performance indicators as cost func￾tions. We design the evaluation function criteria based on the evaluation criteria and obtain the solution using a particle swarm optimization algorithm with variable weight. To address the problem in which a UAV approaches a danger zone, we introduce a repulsion field into the cost function to ensure safety. The simulation results show that the proposed method can effectively avoid obstacles within the constraint conditions and perform dynamic calculations in a complic￾ated environment. Keywords: trajectory planning; receding horizon control; VORONOI graph; variable weight; particle swarm optimiza￾tion; artificial potential field 无人机 (UAV) 航迹规划 (trajectory planning) 是指在初始状态、任务目标、威胁区和一些已知 环境信息的情况下获得性能最优的规划问题，是 任务规划 (mission planning) 系统的关键技术之 一。其在任务规划系统中起着非常重要的作用， 也是实现无人机智能导航并且完成任务的技术保 障。基于飞行安全的需要，综合考虑障碍物、无 收稿日期：2017−08−31. 网络出版日期：2018−04−02. 基金项目：国家自然科学基金项目 (61402537). 通信作者：秦小林. E-mail：qinxl@casit.ac.cn. 第 13 卷第 4 期 智 能 系 统 学 报 Vol.13 No.4 2018 年 8 月 CAAI Transactions on Intelligent Systems Aug. 2018