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Tracking maneuvering target in real time autonomously and accurately in an uncertain environment is one of the challenging missions for unmanned aerial vehicles(UAVs).In this paper,aiming to address the control problem of maneuvering target tracking and obstacle avoidance,an online path planning approach for UAV is developed based on deep reinforcement learning.Through end-to-end learning powered by neural networks,the proposed approach can achieve the perception of the environment and continuous motion output control.This proposed approach includes:(1)A deep deterministic policy gradient(DDPG)-based control framework to provide learning and autonomous decision-making capa-bility for UAVs;(2)An improved method named MN-DDPG for introducing a type of mixed noises to assist UAV with exploring stochastic strategies for online optimal planning;and(3)An algorithm of task-decomposition and pre-training for efficient transfer learning to improve the generalization capability of UAV's control model built based on MN-DDPG.The experimental simulation results have verified that the proposed approach can achieve good self-adaptive adjustment of UAV's flight attitude in the tasks of maneuvering target tracking with a significant improvement in generalization capability and training efficiency of UAV tracking controller in uncertain environments. 相似文献
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In this paper, based on a bidirectional parallel multi-branch feature pyramid network (BPMFPN), a novel one-stage object detector called BPMFPN Det is proposed for real-time detection of ground multi-scale targets by swarm unmanned aerial vehicles (UAVs). First, the bidirectional parallel multi-branch convolution modules are used to construct the feature pyramid to enhance the feature expression abilities of different scale feature layers. Next, the feature pyramid is integrated into the single-stage object detection framework to ensure real-time performance. In order to validate the effectiveness of the proposed algorithm, experiments are conducted on four datasets. For the PASCAL VOC dataset, the proposed algorithm achieves the mean average precision (mAP) of 85.4 on the VOC 2007 test set. With regard to the detection in optical remote sensing (DIOR) dataset, the proposed algorithm achieves 73.9 mAP. For vehicle detection in aerial imagery (VEDAI) dataset, the detection accuracy of small land vehicle (slv) targets reaches 97.4 mAP. For unmanned aerial vehicle detection and tracking (UAVDT) dataset, the proposed BPMFPN Det achieves the mAP of 48.75. Compared with the previous state-of-the-art methods, the results obtained by the proposed algorithm are more competitive. The experimental results demonstrate that the proposed algorithm can effectively solve the problem of real-time detection of ground multi-scale targets in aerial images of swarm UAVs. 相似文献
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To help optimize the spacecraft design and reduce the risk of spacecraft mission failure, a new approach to assess the survivability of spacecraft in orbit is presented here, including the following three steps:1) Sensitivity Analysis of spacecraft. A new sensitivity analysis method, a ray method based on virtual outer wall, is presented here. Using rays to simulate the debris cloud can effectively address the component shadowing issues. 2) Component Vulnerability analysis of spacecraft. A function"Component functional reduction degree — Component physical damage degree"is provided here to clearly describe the component functional reduction. 3) System-level Survivability Assessment of spacecraft. A new method based on expert knowledge reasoning, instead of traditional artificial failure tree method, is presented here to greatly improve the efficiency and accuracy of calculation. 相似文献
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高技术条件下的装备保障 总被引:1,自引:0,他引:1
通过分析研究高技术条件下装备保障的特点,概要阐述了外军装备保障的发展趋势,重点分析了我军装备保障的现状和发展中存在的问题,提出了装备保障发展的建设性意见. 相似文献
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陀螺随机漂移误差模型建模方法研究 总被引:9,自引:0,他引:9
论述了自60年代以来的几种主要陀螺仪漂移误差模型建模的方法,介绍了各种方法的特点及建模公式,有助于惯导系统中陀螺漂移误差模型的建立. 相似文献
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介绍了一种用于聚氨酯泡沫塑料衬垫的粘弹性有限元分析程序,利用积分本构关系计算了轴对称衬垫中的应力松弛。从数值结果可见,当衬垫贮存1000小时以后,装配应力大约松弛了25%。 相似文献
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为量化无动力滑翔飞行器末制导初始参数不确定性的综合影响,提升飞行器落点精度,提出基于不确定性的末制导初始参数优化设计方法。面向飞行器末端高动态打击需求,采用落角约束下的滑模变结构导引律进行实时弹道成型,进而考虑末制导初始参数的不确定性。以落点有效毁伤半径概率和落点圆概率偏差为多优化目标,建立基于不确定性的末制导初始参数及制导律参数优化模型。针对这一不确定性优化模型,研究利用高效全局优化和蒙特卡洛方法,给出末制导初始参数及制导律参数的最优设计方案。仿真结果表明:该方法能显著提升落点精度,为方案设计阶段飞行器末制导交接点的选取提供决策支持。 相似文献