Guidance path-planning and following are two core technologies used for controlling un-manned aerial vehicles(UAVs)in both military and civilian applications.However,only a few approaches treat both the technologies s...Guidance path-planning and following are two core technologies used for controlling un-manned aerial vehicles(UAVs)in both military and civilian applications.However,only a few approaches treat both the technologies simultaneously.In this study,an innovative hybrid gradient vector fields for path-following guidance(HGVFs-PFG)algorithm is proposed to control fixed-wing UAVs to follow a generated guidance path and oriented target curves in three-dimensional space,which can be any combination of straight lines,arcs,and helixes as motion primitives.The algorithm aids the creation of vector fields(VFs)for these motion primitives as well as the design of an effective switching strategy to ensure that only one VF is activated at any time to ensure that the complex paths are followed completely.The strategies designed in earlier studies have flaws that prevent the UAV from following arcs that make its turning angle too large.The proposed switching strategy solves this problem by introducing the concept of the virtual way-points.Finally,the performance of the HGVFs-PFG algorithm is verified using a reducedorder autopilot and four representative simulation scenarios.The simulation considers the constraints of the aircraft,and its results indicate that the algorithm performs well in following both lateral and longitudinal control,particularly for curved paths.In general,the proposed technical method is practical and competitive.展开更多
现有的standoff跟踪制导方法在进行机动目标跟踪时不能同时满足响应速度和稳态精度的要求.对用于航路跟踪的参考点制导法(RPG,Reference Point Guidance)进行改进,推导了无人机(UAV,Unmanned Aerial Vehicle)在跟踪机动目标时的横侧向...现有的standoff跟踪制导方法在进行机动目标跟踪时不能同时满足响应速度和稳态精度的要求.对用于航路跟踪的参考点制导法(RPG,Reference Point Guidance)进行改进,推导了无人机(UAV,Unmanned Aerial Vehicle)在跟踪机动目标时的横侧向制导规律.采用二阶非线性微分方程对UAV和目标相对距离的调节过程进行建模,在此基础上分析了改进后RPG的渐近稳定性.仿真结果表明,相比Lyapunov向量场(LVFG,Lyapunov Vector Field Guidance)和模型预测控制(MPC,Model-based Predictive Control)的制导方法,改进RPG的跟踪误差和时间乘以误差绝对值积分(ITAE,Integrated Time Absolute Error)指标均优于LVFG和MPC.因此,所提制导规律能够对机动目标的运动进行有效补偿,并具有更快的响应速度、更高的稳态精度和较好的实时性.展开更多
为实现无人装备在野外环境下对非结构化道路进行自动、普适和精准的识别与导向,提出了一种基于图推模型与智能寻优的野外场景道路导向算法。首先将图像分割为同质超像素块,对超像素块的多特征进行融合,构造训练集;改进传统拉普拉斯支持...为实现无人装备在野外环境下对非结构化道路进行自动、普适和精准的识别与导向,提出了一种基于图推模型与智能寻优的野外场景道路导向算法。首先将图像分割为同质超像素块,对超像素块的多特征进行融合,构造训练集;改进传统拉普拉斯支持向量机算法,结合超像素块位置信息动态选取道路区域超像素种子块,训练超像素块的多类别分类回归器和相邻超像素的一致性回归器;结合两种回归器的回归值构造马尔可夫随机场的能量函数,再利用标准图割算法迭代求取最小化能量函数,实现初始道路推理分割;结合道路初分割结果,依据人对道路的直观感知,设定约束条件构造目标函数,利用差分免疫克隆进化算法智能寻优提取道路的导向线。在南京珠山采集的数据和DARPA Grand Challenge数据库上进行检测,并与经典算法的道路导向效果进行定性和定量比较,结果表明该算法在野外环境下对非结构化道路的导向线提取精度总体达91.79%以上,相比于经典算法,检测精准度分别提升48.1%和35.5%,算法处理效率分别提升98.6%和97.8%,在检测的实时性与精准度问题上实现了平衡,具有较强的应用前景。展开更多
基金the support of the National Natural Science Foundation of China under Grant No.62076204 and Grant No.62006193in part by the Postdoctoral Science Foundation of China under Grants No.2021M700337in part by the Fundamental Research Funds for the Central Universities under Grant No.3102019ZX016。
文摘Guidance path-planning and following are two core technologies used for controlling un-manned aerial vehicles(UAVs)in both military and civilian applications.However,only a few approaches treat both the technologies simultaneously.In this study,an innovative hybrid gradient vector fields for path-following guidance(HGVFs-PFG)algorithm is proposed to control fixed-wing UAVs to follow a generated guidance path and oriented target curves in three-dimensional space,which can be any combination of straight lines,arcs,and helixes as motion primitives.The algorithm aids the creation of vector fields(VFs)for these motion primitives as well as the design of an effective switching strategy to ensure that only one VF is activated at any time to ensure that the complex paths are followed completely.The strategies designed in earlier studies have flaws that prevent the UAV from following arcs that make its turning angle too large.The proposed switching strategy solves this problem by introducing the concept of the virtual way-points.Finally,the performance of the HGVFs-PFG algorithm is verified using a reducedorder autopilot and four representative simulation scenarios.The simulation considers the constraints of the aircraft,and its results indicate that the algorithm performs well in following both lateral and longitudinal control,particularly for curved paths.In general,the proposed technical method is practical and competitive.
文摘现有的standoff跟踪制导方法在进行机动目标跟踪时不能同时满足响应速度和稳态精度的要求.对用于航路跟踪的参考点制导法(RPG,Reference Point Guidance)进行改进,推导了无人机(UAV,Unmanned Aerial Vehicle)在跟踪机动目标时的横侧向制导规律.采用二阶非线性微分方程对UAV和目标相对距离的调节过程进行建模,在此基础上分析了改进后RPG的渐近稳定性.仿真结果表明,相比Lyapunov向量场(LVFG,Lyapunov Vector Field Guidance)和模型预测控制(MPC,Model-based Predictive Control)的制导方法,改进RPG的跟踪误差和时间乘以误差绝对值积分(ITAE,Integrated Time Absolute Error)指标均优于LVFG和MPC.因此,所提制导规律能够对机动目标的运动进行有效补偿,并具有更快的响应速度、更高的稳态精度和较好的实时性.
文摘为实现无人装备在野外环境下对非结构化道路进行自动、普适和精准的识别与导向,提出了一种基于图推模型与智能寻优的野外场景道路导向算法。首先将图像分割为同质超像素块,对超像素块的多特征进行融合,构造训练集;改进传统拉普拉斯支持向量机算法,结合超像素块位置信息动态选取道路区域超像素种子块,训练超像素块的多类别分类回归器和相邻超像素的一致性回归器;结合两种回归器的回归值构造马尔可夫随机场的能量函数,再利用标准图割算法迭代求取最小化能量函数,实现初始道路推理分割;结合道路初分割结果,依据人对道路的直观感知,设定约束条件构造目标函数,利用差分免疫克隆进化算法智能寻优提取道路的导向线。在南京珠山采集的数据和DARPA Grand Challenge数据库上进行检测,并与经典算法的道路导向效果进行定性和定量比较,结果表明该算法在野外环境下对非结构化道路的导向线提取精度总体达91.79%以上,相比于经典算法,检测精准度分别提升48.1%和35.5%,算法处理效率分别提升98.6%和97.8%,在检测的实时性与精准度问题上实现了平衡,具有较强的应用前景。
