Automatic UAV Positioning with Encoded Sign as Cooperative Target

In order to achieve the goal that unmanned aerial vehicle(UAV)automatically positioning during power inspection,a visual positioning method which utilizes encoded sign as cooperative target is proposed.Firstly,we discuss how to design the encoded sign and propose a robust decoding algorithm based on contour.Secondly,the Adaboost algorithm is used to train a classifier which can detect the encoded sign from image.Lastly,the position of UAV can be calculated by using the projective relation between the object points and their corresponding image points.Experiment includes two parts.First,simulated video data is used to verify the feasibility of the proposed method,and the results show that the average absolute error in each direction is below 0.02 m.Second,a video,acquired from an actual UAV flight,is used to calculate the position of UAV.The results show that the calculated trajectory is consistent with the actual flight path.The method runs at a speed of 0.153 sper frame.

Function of Cooperative Learning in Developing Positive Affect

This paper focus on the function of cooperative learning in developing positive affect,Including reducing anxiety,increasing motivation,facilitating the development of positive attitudes toward learning and language learning,promoting self-esteem,as well as supporting different learning styles and encouraging perseverance in the difficult and confusing process of learning a foreign language.

A Distributed Cooperative Localization Algorithm for Mobile Multi-platforms Oriented to Unpredicted Communication Topology

The cooperative localization(CL)is affected by the communication topology among the platforms.Based on the unscented Kalman filtering,the distributed CL(DCL)oriented to the unpredicted communication topology is investigated.To improve the adaptability,the character of the look-up Cholesky decomposition is exploited for the covariance matrix decomposing.Then,the distributed U transformation can be dynamically implemented according to the available communication topology.In the proposed algorithm,the global information is not required for the individual,and only the available information from the neighbor is used.Each platform’s state can be estimated independently.The error covariance of the state estimates can be updated in the single platform.The algorithm is adaptive to any serial communication topologies where the measuring to the measured platform is a starting path.The applicability of the proposed algorithm to unpredicted communication topology is improved,remaining equivalent localization performance to free connection communication.

Improved GNSS Cooperation Positioning Algorithm for Indoor Localization

For situations such as indoor and underground parking lots in which satellite signals are obstructed,GNSS cooperative positioning can be used to achieve highprecision positioning with the assistance of cooperative nodes.Here we study the cooperative positioning of two static nodes,node 1 is placed on the roof of the building and the satellite observation is ideal,node 2 is placed on the indoor windowsill where the occlusion situation is more serious,we mainly study how to locate node 2 with the assistance of node 1.Firstly,the two cooperative nodes are located with pseudo-range single point positioning,and the positioning performance of cooperative node is analyzed,therefore the information of pseudo-range and position of node 1 is obtained.Secondly,the distance between cooperative nodes is obtained by using the baseline method with double-difference carrier phase.Finally,the cooperative location algorithms are studied.The Extended Kalman Filtering(EKF),Unscented Kalman Filtering(UKF)and Particle Filtering(PF)are used to fuse the pseudo-range,ranging information and location information respectively.Due to the mutual influences among the cooperative nodes in cooperative positioning,the EKF,UKF and PF algorithms are improved by resetting the error covariance matrix of the cooperative nodes at each update time.Experimental results show that after being improved,the influence between the cooperative nodes becomes smaller,and the positioning performance of the nodes is better than before.

A whole-net cooperative positioning method based on CDGNSS and inter-vehicle ranging

Relative positioning is recognized as an important issue for vehicles in urban environments.Multi-vehicle Cooperative Positioning(CP)techniques which fuse the Global Navigation Satellite System(GNSS)and inter-vehicle ranging have attracted attention in improving the performance of baseline estimation between vehicles.However,current CP methods estimate the baselines separately and ignore the interactions among the positioning information of different baselines.These interactions are called’information coupling’.In this work,we propose a new multivehicle precise CP framework using the coupled information in the network based on the Carrier Differential GNSS(CDGNSS)and inter-vehicle ranging.We demonstrate the benefit of the coupled information by deriving the Cramer-Rao Lower Bound(CRLB)of the float estimation in CP.To fully use this coupled information,we propose a Whole-Net CP(WN-CP)method which consists of the Whole-Net Extended Kalman Filter(WN-EKF)as the float estimation filter,and the Partial Baseline Fixing(PBF)as the ambiguity resolution part.The WN-EKF fuses the measurements of all baselines simultaneously to improve the performance of float estimation,and the PBF strategy fixes the ambiguities of the one baseline to be estimated,instead of full ambiguity resolution,to reduce the computation load of ambiguity resolution.Field tests involving four vehicles were conducted in urban environments.The results show that the proposed WN-CP method can achieve better performance and meanwhile maintain a low computation load compared to the existing methods.

Robot Position Control Using Force Information for Cooperative Work in Remote Robot Systems with Force Feedback

This paper proposes robot position control using force information for cooperative work between two remote robot systems with force feedback in each of which a user operates a remote robot by using a haptic interface device while observing work of the robot with a video camera. We also investigate the effect of the proposed control by experiment. As cooperative work, we deal with work in which two robots carry an object together. The robot position control using force information finely adjusts the position of the robot arm to reduce the force applied to the object. Thus, the purpose of the control is to avoid large force so that the object is not broken. In our experiment, we make a comparison among the following three cases in order to clarify how to carry out the control effectively. In the first case, the two robots are operated manually by a user with his/her both hands. In the second case, one robot is operated manually by a user, and the other robot is moved automatically under the proposed control. In the last case, the object is carried directly by a human instead of the robot which is operated by the user in the second case. As a result, experimental results demonstrate that the control can help each system operated manually by the user to carry the object smoothly.

A Stochastic Approach for Cooperative Position Estimation of Multiple Mobile Robots

This paper proposes the cooperative position estimation of a group of mobile robots, which pertbrms disaster relief tasks in a wide area. When searching the wide area, it becomes important to know a robot＇s position correctly. However, for each mobile robot, it is impossible to know its own position correctly. Therefore, each mobile robot estimates its position from the data of sensor equipped on it. Generally, the sensor data is incorrect since there is sensor noise, etc. This research considers two types of the sensor data errors from omnidirectional camera. One is the error of white noise of the image captured by omnidirectional camera and so on. Another is the error of position and posture between two omnidirectional cameras. To solve the error of latter case, we proposed a self-position estimation algorithm for multiple mobile robots using two omnidirectional cameras and an accelerometer. On the other hand, to solve the error of the former case, this paper proposed an algorithm of cooperative position estimation for multiple mobile robots. In this algorithm, each mobile robot uses two omnidirectional cameras to observe the surrounding mobile robot and get the relative position between mobile robots. Each mobile robot estimates its position with only measurement data of each other mobile robots. The algorithm is based on a Bayesian filtering. Simulations of the proposed cooperative position estimation algorithm for multiple mobile robots are performed. The results show that position estimation is possible by only using measurement value from each other robot.

Cooperative localization against GPS signal loss in multiple UAVs flight

Based on multiple unmanned aerial vehicles（UAVs） flight at a constant altitude,a fault-tolerant cooperative localization algorithm against global positioning system（GPS） signal loss due to GPS receiver malfunction is proposed.Contrast to the traditional means with single UAV,the proposed method is based on the use of inter-UAV relative range measurements against GPS signal loss and more suitable for the small-size and low-cost UAV applications.Firstly,for re-localizing an UAV with a malfunction in its GPS receiver,an algorithm which makes use of any other three healthy UAVs in the cooperative flight as the reference points for re-localization is proposed.Secondly,by using the relative ranges from the faulty UAV to the other three UAVs,its horizontal location can be determined after the GPS signal is lost.In order to improve an accuracy of the localization,a Kalman filter is further exploited to provide the estimated location of the UAV with the GPS signal loss.The Kalman filter calculates the variance of observations in terms of horizontal dilution of positioning（HDOP） automatically.Then,during each discrete computing time step,the best reference points are selected adaptively by minimizing the HDOP.Finally,two simulation examples in Matlab/Simulink environment with five UAVs in cooperative flight are shown to evaluate the effectiveness of the proposed method.

Global asymptotic stability of positive equilibrium in a 3-species cooperating model with time delay

The asymptotic behavior of the time-dependent solution for a 3-species cooperating model was investigated with the effects of both diffusion and time delay taken into consideration. We proved the global asymptotic stability of a positive steady-state solution to the model problem by using coupled upper and lower solutions for a more general reaction-diffusion system that gives a common framework for 3-species cooperating model problems. The result of global asymptotic stability implies that the model system coexistence is permanent. Some global asymptotic stability results for 2-species cooperating reaction-diffusion systems are included in the discussion, and some known results are extended.

无人机系统控制研究综述

无人机(UAV)作为集技术创新和应用场景多元化为一体的低成本、高效率的明星产品,正引领着当前社会中众多行业的变革。这一变革的核心在于无人机系统控制的不断研究与进步。系统控制是无人机技术中至关重要的一个环节,它涉及到飞行稳定性、导航精度、任务执行能力以及安全性等多个方面。对近些年来的相关文献进行了综述和分析,首先,阐述了无人机系统控制的发展需求,并对无人机系统控制发展及控制架构两部分进行了详细的介绍;接着,围绕无人机系统控制的关键技术问题,即导航定位、跟踪控制、自适应控制、集群协同控制,展开全面概述和深入剖析;最后,对无人机系统控制的发展趋势进行了展望。

基于车路协同技术的超高速公路虚拟轨道系统研究

为保障超高速公路行车安全,论证虚拟轨道系统在超高速公路上应用的可行性,以车路协同技术为基础,搭建了超高速公路虚拟轨道系统模型。通过建立实时车辆动态坐标系,结合车辆偏离安全区域,计算出车辆横向偏转角度阈值;利用定位系统和路侧单元检测系统,研究了微波测距模块采样频率和安装间距随车速变化的情况。结果表明:在虚拟轨道单侧安全区域为10 cm时,小型两厢车、小型三厢车、中型车、中大型车、大型车车辆横向偏转的最大安全角度范围分别是1.43°~1.59°、1.30°~1.40°、1.22°~1.33°、1.17°~1.25°、1.10°~1.20°;当车辆行驶速度为180 km/h时,路侧单元传感器的采样频率高于27.72 Hz,传感器的安装距离小于6.87 m时可满足要求。

基于多元融合的企业技术创新合作预测方法研究

合理的技术创新合作预测方法是企业寻找合适的技术创新合作伙伴以提升技术创新绩效的有效手段。论文基于企业专利数据,构建专利所有权人共现网络,应用Katz指标计算企业之间的路径相似性,应用TF-IDF算法构建企业关键词向量,结合余弦相似性计算企业之间的内容相似性,应用社会网络分析方法中的中心性指标计算企业的位置相似性,将三者进一步融合得到企业之间合作的潜在可能性。通过对石墨烯领域企业专利数据分析预测企业间合作的可能性,证实该方法有效,AUC指标值为0.7242,优于单一指标相似性推荐方法,能够提升合作推荐中合适匹配的精确度。

战略叙事展演与秩序地位竞争:中小国家的领导力构建与领导模式演变

中小国家为何可以构建以及如何构建领导力?文章以东盟为中心展开的区域合作实践为例,发现以战略叙事展演为主要表现形式的秩序地位竞争是中小国家在区域合作实践中构建领导力并推动领导模式演变的核心机制。印尼、新加坡与越南先后通过“抗御力”“经济一体化”“团结东盟成员”的战略叙事展演向其他域内国家证明其对地区需求的识别,以此进行秩序地位竞争与领导力构建,使区域合作实践呈现域内强国领导、二元合作领导与多元合作领导的变化。中小国家能否在区域合作实践中成功构建领导力,与其战略叙事展演能否匹配体系压力形塑的地区需求并得到域内其他中小国家支持、其战略叙事竞争能否经受其他中小国家的挑战有关。文章为理解中小国家随着体系压力及地区需求变化调整其区域合作实践提供了理论解释,也为最大限度维护与中小国家的合作进程提供了实证依据。

非合作式双基地雷达系统设计与验证

非合作双基地雷达在目标探测的安全稳健等方面展现了优越的性能。本文首先从双基地雷达同步这一关键问题出发,分析合作和非合作双基地雷达的差异,推导非合作双基地雷达目标定位原理。进一步设计非合作双基地雷达系统,解决时空同步问题,提取直达波和目标散射波的特征参数,解析两者路径差、接收方位、俯仰等信息,最终求解目标接收距离,实现目标定位。雷达协同探测试验采用实测数据验证了非合作双基地技术体制的可行性,为目标探测定位提供了有效的方法。

共赢促进合作的认知计算机制:互惠中积极期望与社会奖赏的作用

在社会互动中,人们常表现出有条件的合作行为,即只有在预期他人也合作时人们才愿意合作。当前该过程的认知机制尚不明确。本文采用多回合版本的囚徒困境范式,两项实验均表明个体的合作行为随合作者合作行为的提高而提高。认知计算模型显示个体同时采用了一阶信念(只根据他人过去的行为)与二阶信念(既根据他人过去的行为,也考虑自己的行为对他人产生的影响)去更新他们对合作者的合作概率的期望。结果显示个体的有条件合作行为的提升由积极期望(即合作成功使得个体对合作者建立了积极的期望)与社会奖赏(由合作本身带来的额外奖励)共同驱动。这些结果揭示了有条件的合作行为的计算认知学习机制,阐明了积极期望和社会奖赏对合作的促进作用,能为社会中各领域合作的成功推动提供了重要的科学证据与参考价值。

超网络中心性度量的υ-Position值方法

利用合作博弈理论的分配规则如Shapley值、Banzhaf值等来度量政治、经济和社会网络中节点的中心性或者重要性是识别网络中关键节点的一类重要方法。考虑到在超网络中代表各类组织的超边在网络中发挥的作用不同,本文研究了超网络博弈上一类广义Position值的分配规则,被称为υ-position值。它可以作为网络中度值测度的一类推广,以此来度量网络中参与者的中心性和相对重要性。其次,证明了超网络结构上类Shapley-position值可由分支超边指数和局部平衡超边贡献两个性质所唯一刻画。最后,举例分析了υ-position值在超网络中心性测度中的应用。

非合作线谱声源分布式定位方法

非合作目标定位是水声定位领域的研究热点。为充分挖掘并利用各类可观测参数,提高非合作目标定位能力,提出一种基于目标频率变化信息的非合作目标定位方法。该方法针对匀速直线运动目标的定位问题,首先根据多普勒频移原理,建立观测频率与目标辐射频率、目标运动速度、位置之间的函数映射关系,利用最小均方准则建立目标函数,通过优化算法估计分布式定位系统中各个测量单元与目标运动轨迹的致近点距离,最后综合各观测节点的测距结果,构建几何定位模型,求得目标运动轨迹的解析解。通过仿真分析,证明了该方法的有效性,并指出了该方法需要目标通过与各测量单元的致近点才能获得较好的距离估计能力。文中分析了不同频率估计精度对定位精度的影响,结果表明,提出的定位方法对测频精度具有一定的容限,是一种高精度的非合作线谱声源定位方法。

基于5G信号异步时钟误差补偿的多无人机协同定位方法

5G信号可为多无人机(UAV)提供精确的相对测距/测角信息,进而提升其在卫星接收机故障时的协同定位精度。然而,协同网络中各节点的5G通讯时钟往往是异步的,这会对测距/测角信息的精度产生较大影响,进而降低融合性能。针对上述问题,提出了一种基于5G信号异步时钟误差补偿的多无人机协同定位方法。首先,构建了基于5G信号异步时钟误差的相对测距模型,并利用相对测距/测角信息以及高度计信息建立了从机的位置观测方程。在此基础上,通过设计迭代扩展卡尔曼滤波器(IEKF)实现了对从机惯导系统(INS)误差以及异步时钟误差的联合最优估计,提高了从机定位精度。仿真结果表明,相比于传统方法,所提方法将从机的水平定位精度提高了25.3%。

基于测距与GNSS信息融合的车联网协同定位技术

为了提高车联网中车辆定位的精度,提出了基于车载雷达测距信息与全球卫星导航系统(global navigation satellite system,GNSS)信息融合的车联网协同定位方法。该方法使用极大似然估计策略建立数学模型,其本质是一个非线性优化问题。将其化简为具有多个二次等式约束的二次规划问题,并给出一种半正定松弛方法,可以高效地近似求解原问题,最后通过特征值分解法进一步改进该近似解。仿真结果表明,该信息融合方法得到的协同定位比线性化加权最小二乘方法的定位精度有显著提高;且能达到基于较大数据集的BP(back propagation)神经网络定位方法的定位精度,但无须事先训练模型,可实现高精度实时定位。