A survey on tracking control of unmanned underwater vehicles: Experiments-based approach

This paper aims to provide a review of the conceptual design and theoretical framework of the main control schemes proposed in the literature for unmanned underwater vehicles (UUVs). Additionally, the objective of the paper is not only to present an overview of the recent control architectures validated on UUVs but also to give detailed experimental-based comparative studies of the proposed control schemes. To this end, the main control schemes, including proportional–integral–derivative (PID) based, sliding mode control (SMC) based, adaptive based, observation-based, model predictive control (MPC) based, combined control techniques, are revisited in order to consolidate the principal efforts made in the last two decades by the automatic control community in the field. Besides implementing some key tracking control schemes from the classification mentioned above on Leonard UUV, several real-time experimental scenarios are tested, under different operating conditions, to evaluate and compare the efficiency of the selected tracking control schemes. Furthermore, we point out potential investigation gaps and future research trends at the end of this survey.     

Shared human–robot path following control of an unmanned ground vehicle

This paper considers the shared path following control of an unmanned ground vehicle by a single person. A passive measure of human intent is used to blend the human and machine inputs in a mixed initiative approach. The blending law is combined with saturated super-twisting sliding mode speed and heading controllers, so that exogenous disturbances can be counteracted via equivalent control. It is proven that when the proposed blending law is used, the combined control signals from both the human and automatic controller respect the actuator magnitude constraints of the machine. To demonstrate the approach, shared control experiments are performed using an unmanned ground vehicle, which follows a lawn mower pattern shaped path.     

Visual detection and tracking with UAVs,following a mobile object

ABSTRACT

Conception and development of an Unmanned Aerial Vehicle (UAV) capable of detecting, tracking and following a moving object with unknown dynamics is presented in this work, considering a human face as a case of study. Object detection is accomplished by a Haar cascade classifier. Once an object is detected, it is tracked with the help of a Kalman Filter (KF), and an estimation of the relative position with respect to the target is obtained. A linear controller is used to validate the proposed vision scheme and for regulating the aerial robot's position in order to keep a constant distance with respect to the mobile target, employing as well the extra available information from the embedded sensors. The proposed system was extensively tested in real-time experiments, through different conditions, using a commercial quadcopter connected via wireless to a ground station running under the Robot Operative System (ROS). The proposed overall strategy shows a good performance even under disadvantageous conditions as outdoor flight, being robust against illumination changes, image noise and the presence of other people in the scene.     

CO2 abatement and CH4 recovery at vehicle exhausts: Comparison and characterization of Ru powder and pellet catalysts

The catalytic conversion of CO₂ to CH₄ (Sabatier reaction) has been studied to develop an after-treatment process at vehicles exhausts. Three different formulations of Ru commercial catalysts, two in powder and one in pellets shape, were tested and characterized by means of X-ray powder diffraction, scanning electron microscopy (SEM-EDX), N₂ adsorption at −196 °C and temperature-programmed reduction (TPR).Experimental results show a high CO₂ conversion (X_CO2 = 0.96 @ T = 280 °C) for one powder catalyst formulation whereas the other one has maximum CO₂ conversion = 0.69. In both cases a high CH₄ selectivity is measured. High CO₂ conversion (X_CO2 = 0.92 @ T = 300 °C) is obtained also with pellet catalysts but only at lower GHSV values. The different behavior of the catalysts was ascribed to the different physicochemical properties and the key parameters for the application development of the process were identified. In particular, the possibility to use pellets or monolithic reactors, thus minimizing the pressure drops in the reactor, makes possible a commercial application in the treatment of vehicles exhausts.     

A terrain risk assessment method for military surveillance applications for mobile assets

This study proposes an analytical and flexible terrain risk assessment method for military surveillance applications for mobile assets. Considering the risk as the degree of possibility of insurgent presence, the assessment method offers an efficient evaluation of risk in the surrounding terrain for military combat operating posts or observation posts. The method is designed for unmanned aerial vehicles as the surveillance assets of choice to improve the effectiveness of their use. Starting with the area map and geographical data, the target terrain is first digitized for space representation. Then the data of nine geographical parameters are used to formulate five contributing risk factors. These factors are incorporated in an analytical framework to generate a composite map with risk scores that reveal the potential high-risk spots in the terrain. The proposed method is also applied to a real-life case study of COP Kahler in Afghanistan, which was a target for insurgent attacks in 2008. The results confirm that when evaluated with the developed method, the region that the insurgents used to approach COP Kahler has high concentration of high-risk cells.     

车联网环境下基于节点认知交互的路由算法

针对车联网（IoV）环境下消息传输效率低下、网络资源开销较大等诸多问题，提出一种适用于城市交通场景下基于车辆节点认知交互的路由算法。首先，依据信任理论提出节点认知交互度的概念，并在此基础上对车联网中的车辆节点进行分类，赋予它们不同的认知交互度初值；同时还引入车辆节点交互时间、交互频率、车辆节点物理间隔距离、间隔跳数以及消息生存时间等影响因子，进而构建了车辆节点认知交互评估模型。基于该模型计算并更新节点的认知交互度，并通过比较对应车辆节点间的认知交互度值来选取认知交互度相对较高的邻居节点作为中继节点进行消息转发。仿真实验结果表明，与Epidemic和Prophet路由算法相比，所提路由算法有效提高了消息投递率并降低了消息投递时延，同时显著降低了网络资源的开销，有助于提升车联网环境的消息传输质量。     

频率域电磁法在采空区探测中的应用

隐伏采空区的探测已成为近年来研究课题,采用无人机搭载发射装置,高空平面布置探测线和测点,克服在陡峭的山坡难以布置探测工作,消除地形地貌及其它条件对探测工作带的误差。对已知空区的探测和分析,验证了该方法的有效性和精确程度,为后续其它探测提供了频率域区间和参数校正。研究表明:此方法具有适用性强、工作效率高和精确度高优点,是一种可用于隐伏空区的探测方法,分析过程中可以辨别采空区变形情况。     

基于四旋翼无人机平台的实时多目标检测算法

随着无人机技术和深度学习技术的发展,基于深度学习的多目标检测算法在工业无人机中得到了广泛的应用。针对目前基于深度学习的多目标检测算法占用大量计算量资源,难以在算力有限的中小型无人机平台上实时运行的问题,分析了深度学习算法在低功耗CPU上的耗时,提出一种卷积神经网络计算优化方法。在机载计算机中进行仿真,结果表明在检测效果基本不变的条件下,算法帧率达到了56FPS,实现了无人机平台上的实时多目标检测。     

航空物探遥感地质资料定密工作初探与实践

在地质调查信息化与地质资料社会化服务工作不断发展的背景下,航空物探遥感地质资料的开发利用与保密管理工作矛盾日益凸显。本文以航空物探遥感资料定密为研究重点,总结了地质、测绘地理信息、海洋等工作领域的定密工作情况,基于对航空物探遥感地质资料分类结果提取了共性资料涉密要素/事项,根据保密法规的相关规定与要求,初步建立了以比例尺/空间精度、测绘地理信息要素、航空物探遥感专业要素为主的定密判定要素集,提出了航空物探遥感地质资料的定密工作原则与参考准则,以期为航空物探遥感地质信息共享服务提供必要的工作支撑。     

Bottom-up image detection of water channel slope damages based on superpixel segmentation and support vector machine

The operation of water supply channels is threatened by the occasionally occurred slope damages. Timely detection of their occurrence is critical for the rapid enforcement of mitigation measures. However, current practices based on routine inspection and structural heath monitoring are inefficient, laborious and tend to be biased. As an attempt to address the limitations, this paper proposes a bottom-up image detection approach for slope damages, which includes four steps, i.e. superpixel segmentation, feature handcrafting, superpixel classification based on support vector machine (SVM), and slope damage recognition. The approach employs a bottom-up strategy to infer the upper-level slope condition from the classification results of individual superpixels in the bottom level. Experiments were conducted to demonstrate the effectiveness of the approach. The handcrafted feature “LBP + HSV” was demonstrated to be effective in characterizing the image features of slope damages. An SVM model with “LBP + HSV” as input can reliably identify the slope condition in superpixels. Based on the SVM model, the bottom-up strategy achieved high recognition performance, of which the overall accuracy can be up to 91.7%. The proposed approach has potential to facilitate the early and comprehensive awareness of slope damages along the entire route of water channel by the integration with unmanned aerial vehicles.