Autonomous science during large‐scale robotic survey

Today's planetary exploration robots rarely travel beyond the yesterday imagery. However, advances in autonomous mobility will soon permit single‐command site surveys of multiple kilometers. Here scientists cannot see the terrain in advance, and explorer robots must navigate and collect data autonomously. Onboard science data understanding can improve these surveys with image analysis, pattern recognition, learned classification, and information‐theoretic planning. We report on field experiments near Amboy Crater, California, that demonstrate fundamental capabilities for autonomous surficial mapping of geologic phenomena with a visible near‐infrared spectrometer. We develop an approach to “science on the fly'' that adapts the robot's exploration using collected instrument data. We demonstrate feature detection and visual servoing to acquire spectra from dozens of targets without human intervention. The rover interprets spectra onboard, learning spatial models of science phenomena that guide it toward informative areas. It discovers spatial structure (correlations between neighboring regions) and cross‐sensor structure (correlations between different scales). The rover uses surface observations to reinterpret satellite imagery and improve exploration efficiency. © 2011 Wiley Periodicals, Inc.     

Autonomous Four‐Dimensional Mapping and Tracking of a Coastal Upwelling Front by an Autonomous Underwater Vehicle

Coastal upwelling is a wind‐driven ocean process that brings cooler, saltier, and nutrient‐rich deep water upward to the surface. The boundary between the upwelling water and the normally stratified water is called the “upwelling front.” Upwelling fronts support enriched phytoplankton and zooplankton populations, thus they have great influences on ocean ecosystems. Traditional ship‐based methods for detecting and sampling ocean fronts are often laborious and very difficult, and long‐term tracking of such dynamic features is practically impossible. In our prior work, we developed a method of using an autonomous underwater vehicle (AUV) to autonomously detect an upwelling front and track the front's movement on a fixed latitude, and we applied the method in scientific experiments. In this paper, we present an extension of the method. Each time the AUV crosses and detects the front, the vehicle makes a turn at an oblique angle to recross the front, thus zigzagging through the front to map the frontal zone. The AUV's zigzag tracks alternate in northward and southward sweeps, so as to track the front as it moves over time. This way, the AUV maps and tracks the front in four dimensions—vertical, cross‐front, along‐front, and time. From May 29 to June 4, 2013, the Tethys long‐range AUV ran the algorithm to map and track an upwelling front in Monterey Bay, CA, over five and one‐half days. The tracking revealed spatial and temporal variabilities of the upwelling front.     

海洋基本地理特征可视化技术研究

针对海洋工程领域仿真可视化的需求,在OpenGL平台下,以电子海图为基础,对海洋基本地理特征的构建和可视化开展了针对性的研究工作,通过研究海底地形和地物数据的特点,采用了不同的建模方法和实时显示方法。并搭建了二、三维可视化联动的系统架构。最后通过相应的试验工作验证了相关方法的正确性。系统成本低廉、运行高速,可以真实地重建海洋地理特征,为获取和感知准确全面的海洋基本地理特征信息环境打下了基础,具有一定的理论意义和工程应用价值。     

Autonomous collision detection and avoidance for ARAGON USV: Development and field tests

This study addresses the development of algorithms for multiple target detection and tracking in the framework of sensor fusion and its application to autonomous navigation and collision avoidance systems for the unmanned surface vehicle (USV) Aragon. To provide autonomous navigation capabilities, various perception sensors such as radar, lidar, and cameras have been mounted on the USV platform and automatic ship detection algorithms are applied to the sensor measurements. The relative position information between the USV and nearby objects is obtained to estimate the motion of the target objects in a sensor‐level tracking filter. The estimated motion information from the individual tracking filters is then combined in a central‐level fusion tracker to achieve persistent and reliable target tracking performance. For automatic ship collision avoidance, the combined track data are used as obstacle information, and appropriate collision avoidance maneuvers are designed and executed in accordance with the international regulations for preventing collisions at sea (COLREGs). In this paper, the development processes of the vehicle platform and the autonomous navigation algorithms are described, and the results of field experiments are presented and discussed.     

Autonomous Salient Feature Detection through Salient Cues in an HSV Color Space for Visual Indoor Simultaneous Localization and Mapping

For successful simultaneous localization and mapping (SLAM), perception of the environment is important. This paper proposes a scheme to autonomously detect visual features that can be used as natural landmarks for indoor SLAM. First, features are roughly selected from the camera image through entropy maps that measure the level of randomness of pixel information. Then, the saliency of each pixel is computed by measuring the level of similarity between the selected features and the given image. In the saliency map, it is possible to distinguish the salient features from the background. The robot estimates its pose by using the detected features and builds a grid map of the unknown environment by using a range sensor. The feature positions are stored in the grid map. Experimental results show that the feature detection method proposed in this paper can autonomously detect features in unknown environments reasonably well.     

YOLOv4-tiny-based robust RGB-D SLAM approach with point and surface feature fusion in complex indoor environments

Algorithm frameworks based on feature point matching are mature and widely used in simultaneous localization and mapping (SLAM). However, in the complex and changeable indoor environment, feature point matching-based SLAM currently has two major problems, namely, decreased accuracy of pose estimation due to the interference caused by dynamic objects to the SLAM system and tracking loss caused by the lack of feature points in weak texture scenes. To address these problems, herein, we present a robust and real-time RGB-D SLAM algorithm that is based on ORBSLAM3. For interference caused by indoor moving objects, we add the improved lightweight object detection network YOLOv4-tiny to detect dynamic regions, and the dynamic features in the dynamic area are then eliminated in the algorithm tracking stage. In the case of indoor weak texture scenes, while extracting point features the system extracts surface features at the same time. The framework fuses point and surface features to track camera pose. Experiments on the public TUM RGB-D data sets show that compared with the ORB-SLAM3 algorithm in highly dynamic scenes, the root mean square error (RMSE) of the absolute path error of the proposed algorithm improved by an average of 94.08%. Camera pose is tracked without loss over time. The algorithm takes an average of 34 ms to track each frame of the picture just with a CPU, which is suitably real-time and practical. The proposed algorithm is compared with other similar algorithms, and it exhibits excellent real-time performance and accuracy. We also used a Kinect camera to evaluate our algorithm in complex indoor environment, and also showed high robustness and real-time. To sum up, our algorithm can not only deal with the interference caused by dynamic objects to the system but also stably run in the open indoor weak texture scene.     

基于多层交叉熵的网络入侵数据自主防御系统设计

为了提高无线动态压缩感知网络的入侵检测能力,提出一种基于多层交叉熵的网络入侵数据自主防御系统设计方法,构建网络入侵数据检测方法,采用大数据挖掘技术进行无线动态压缩感知网络的入侵大数据挖掘,对挖掘的入侵数据采用频谱超分辨识别方法进行特征提取,构建无线动态压缩感知网络入侵检测的动态多层数据分布结构模型,采用关联映射方法进行网络入侵数据的信号结构重组,结合模糊自适应调度方法进行入侵数据的多层交叉熵调度,根据入侵数据的异常性特征分布实现自主检测和入侵特征定位。采用嵌入式的Linux开发工具进行网络入侵数据自主防御系统设计,结合程序加载和交叉编译实现入侵检测算法的自动读写和检测输出。测试结果表明,采用该方法进行网络入侵数据自主防御系统设计,提高了对入侵数据的检测主动性和准确性,从而提高了网络安全性。     

RANGE–Robust autonomous navigation in GPS‐denied environments

This paper addresses the problem of autonomous navigation of a micro air vehicle (MAV) in GPS‐denied environments. We present experimental validation and analysis for our system that enables a quadrotor helicopter, equipped with a laser range finder sensor, to autonomously explore and map unstructured and unknown environments. The key challenge for enabling GPS‐denied flight of a MAV is that the system must be able to estimate its position and velocity by sensing unknown environmental structure with sufficient accuracy and low enough latency to stably control the vehicle. Our solution overcomes this challenge in the face of MAV payload limitations imposed on sensing, computational, and communication resources. We first analyze the requirements to achieve fully autonomous quadrotor helicopter flight in GPS‐denied areas, highlighting the differences between ground and air robots that make it difficult to use algorithms developed for ground robots. We report on experiments that validate our solutions to key challenges, namely a multilevel sensing and control hierarchy that incorporates a high‐speed laser scan‐matching algorithm, data fusion filter, high‐level simultaneous localization and mapping, and a goal‐directed exploration module. These experiments illustrate the quadrotor helicopter's ability to accurately and autonomously navigate in a number of large‐scale unknown environments, both indoors and in the urban canyon. The system was further validated in the field by our winning entry in the 2009 International Aerial Robotics Competition, which required the quadrotor to autonomously enter a hazardous unknown environment through a window, explore the indoor structure without GPS, and search for a visual target. © 2011 Wiley Periodicals, Inc.     

On Feature Matching and Image Registration for Two‐dimensional Forward‐scan Sonar Imaging

The computer processing of forward‐look sonar video imagery enables significant capabilities in a wide variety of underwater operations within turbid environments. Accurate automated registration of sonar video images to complement measurements from traditional positioning devices can be instrumental in the detection, localization, and tracking of distinct scene targets, building feature maps, change detection, as well as improving precision in the positioning of unmanned submarines. This work offers a novel solution for the registration of two‐dimensional (2‐D) forward‐look sonar images recorded from a mobile platform, by optimization over the sonar 3‐D motion parameters. It incorporates the detection of key features and landmarks, and effectively represents them with Gaussian maps. Improved performance is demonstrated with respect to the state‐of‐the‐art approach utilizing 2‐D similarity transformation, based on experiments with real data.     

The Hierarchical Fast Learning Artificial Neural Network (HieFLANN)—An Autonomous Platform for Hierarchical Neural Network Construction

The hierarchical fast learning artificial neural network (HieFLANN) is a clustering NN that can be initialized using statistical properties of the data set. This provides the possibility of constructing the entire network autonomously with no manual intervention. This distinguishes it from many existing networks that, though hierarchically plausible, still require manual initialization processes. The unique system of hierarchical networks begins with a reduction of the high-dimensional feature space into smaller and manageable ones. This process involves using the K-iterations fast learning artificial neural network (KFLANN) to systematically cluster a square matrix containing the Mahalanobis distances (MDs) between data set features, into homogeneous feature subspaces (HFSs). The KFLANN is used for its heuristic network initialization capabilities on a given data set and requires no supervision. Through the recurring use of the KFLANN and a second stage involving canonical correlation analysis (CCA), the HieFLANN is developed. Experimental results on several standard benchmark data sets indicate that the autonomous determination of the HFS provides a viable avenue for feasible partitioning of feature subspaces. When coupled with the network transformation process, the HieFLANN yields results showing accuracies comparable with available methods. This provides a new platform by which data sets with high-dimensional feature spaces can be systematically resolved and trained autonomously, alleviating the effects of the curse of dimensionality.     

Fault‐tolerant path‐following control for in‐wheel‐motor‐driven autonomous ground vehicles with differential steering

Over the past several decades, the automobile industry has denoted significant research efforts to developing in‐wheel‐motor‐driven autonomous ground vehicles (IWM‐AGVs) with active front‐wheel steering. One of the most fundamental issues for IWM‐AGVs is path following, which is important for automated driving to ensure that the vehicle can track a target‐planned path during local navigation. However, the path‐following task may fail if the system experiences a stuck fault in the active front‐wheel steering. In this paper, a fault‐tolerant control (FTC) strategy is presented for the path following of IWM‐AGVs in the presence of a stuck fault in the active front‐wheel steering. For this purpose, differential steering is used to generate differential torque between the left and right wheels in IWM‐AGVs, and an adaptive triple‐step control approach is applied to realize coordinated lateral and longitudinal path‐following maneuvering. The parameter uncertainties for the cornering stiffness and external disturbances are considered to make the vehicles robust to different driving environments. The effectiveness of the proposed scheme is evaluated with a high‐fidelity and full‐car model based on the veDYNA‐Simulink joint platform.     

基于RGB-D信息的移动机器人SLAM和路径规划方法研究与实现

移动机器人在各种辅助任务中需具备自主定位、建图、路径规划与运动控制的能力。本文利用RGB-D信息和ORB-SLAM算法进行自主定位,结合点云数据和GMapping算法建立环境栅格地图,基于二次规划方法进行平滑可解析的路径规划,并设计非线性控制器,实现了由一个运动底盘、一个RGB-D传感器和一个运算平台组成的自主移动机器人系统。经实验验证,这一系统实现了复杂室内环境下的实时定位与建图、自主移动和障碍物规避。由此,为移动机器人的推广应用提供了一个硬件结构简单、性能良好、易扩展、经济性好、开发维护方便的解决方案。     

Optimization‐based adaptive sliding mode control with application to vehicle dynamics control

This paper presents the design of a new adaptive optimization‐based second‐order sliding mode control algorithm for uncertain nonlinear systems. It is designed on the basis of a second‐order sliding mode control with optimal reaching, with the aim of reducing the control effort while maintaining all the positive aspects in terms of finite‐time convergence and robustness in front of matched uncertainties. These features are beneficial to guarantee good performance in case of vehicle dynamics control, a crucial topic in the light of the increasing demand of semiautonomous and autonomous driving capabilities in commercial vehicles. The new proposal is theoretically analyzed, as well as verified relying on an extensive comparative study, carried out on a realistic simulator of a 4‐wheeled vehicle, in the case of a lateral stability control system.     

Efficient autonomous navigation for planetary rovers with limited resources

Rovers operating on Mars require more and more autonomous features to fulfill their challenging mission requirements. However, the inherent constraints of space systems render the implementation of complex algorithms an expensive and difficult task. In this paper, we propose an architecture for autonomous navigation. Efficient implementations of autonomous features are built on top of the ExoMars path following navigation approach to enhance the safety and traversing capabilities of the rover. These features allow the rover to detect and avoid hazards and perform significantly longer traverses planned by operators on the ground. The efficient navigation approach has been implemented and tested during field test campaigns on a planetary analogue terrain. The experiments evaluated the proposed architecture by autonomously completing several traverses of variable lengths while avoiding hazards. The approach relies only on the optical Localization Cameras stereo bench, a sensor that is found in all current rovers, and potentially allows for computationally inexpensive long‐range autonomous navigation in terrains of medium difficulty.     

级联特征点检测的锥套小目标快速检测定位算法

为提升无人机自主空中加油中锥套小目标的检测精度和实时性,提出了级联网络与特征点检测网络的锥套小目标高精度快速定位算法。该算法设计了锥套目标全局粗定位和局部精定位的两级检测神经网络结构,采用特征点的输出位置误差及特征点群拟合的椭圆参数误差设计网络的损失函数,利用特征点拟合的锥套目标尺寸位置信息修正跟踪算法,提升了跟踪算法的和目标定位的实时性。实验测试结果表明,该定位算法定位成功率在95%以上,定位的精度（预测区域与真实区域的重叠率）在80%以上,定位输出速率达到100 Hz,对于环境的变化有着较强的适应性。因此该算法可以快速准确地进行锥套目标的跟踪,对于无人机空中加油技术的发展具有重要的研究意义。     

Loop closure detection using CNN words

Intelligent Service Robotics - Loop closure detection (LCD) is crucial for the simultaneous localization and mapping system of an autonomous robot. Image features from a convolution neural network...     

High‐precision control of tracked field robots in the presence of unknown traction coefficients

Accurate steering through crop rows that avoids crop damage is one of the most important tasks for agricultural robots utilized in various field operations, such as monitoring, mechanical weeding, or spraying. In practice, varying soil conditions can result in off‐track navigation due to unknown traction coefficients so that it can cause crop damage. To address this problem, this paper presents the development, application, and experimental results of a real‐time receding horizon estimation and control (RHEC) framework applied to a fully autonomous mobile robotic platform to increase its steering accuracy. Recent advances in cheap and fast microprocessors, as well as advances in solution methods for nonlinear optimization problems, have made nonlinear receding horizon control (RHC) and receding horizon estimation (RHE) methods suitable for field robots that require high‐frequency (milliseconds) updates. A real‐time RHEC framework is developed and applied to a fully autonomous mobile robotic platform designed by the authors for in‐field phenotyping applications in sorghum fields. Nonlinear RHE is used to estimate constrained states and parameters, and nonlinear RHC is designed based on an adaptive system model that contains time‐varying parameters. The capabilities of the real‐time RHEC framework are verified experimentally, and the results show an accurate tracking performance on a bumpy and wet soil field. The mean values of the Euclidean error and required computation time of the RHEC framework are equal to 0.0423 m and 0.88 ms, respectively.     

基于组合EKF的自主水下航行器SLAM

针对标准扩展卡尔曼滤波(EKF)在噪声统计特性不准确、系统模型与实际模型无法完全匹配情况下滤波精度严重下降的问题,提出了一种基于Sage-Husa自适应EKF和强跟踪EKF组合的SLAM(同步定位与地图构建)算法.首先建立了AUV(自主水下航行器)的动力学模型、特征模型以及传感器的测量模型,然后通过Hough变换进行特征提取,最终采用组合EKF实现了自主水下航行器的同步定位与地图构建.海试数据仿真试验表明本文所提方法降低了噪声统计特性时变以及模型不精确对系统的影响,提高了SLAM系统的精确性和鲁棒性.     

无人机多机动目标自主探测与跟踪

无人机机载相机图像中机动目标尺寸较小而且会发生显著变化,加上大量的背景噪声干扰,给目标探测和跟踪带来很大困难.针对这些问题,本文提出了一种在无人机机载相机图像序列中自主探测与跟踪多个机动目标的方法.首先,提取目标的图像数字特征并采用级联分类算法进行特征分类,得到目标的强分类器,对目标进行自主探测搜索.然后,基于全局最优关联算法对探测回波进行关联滤波,实现对多个机动目标的跟踪与识别,其中最优关联代价矩阵融合了距离和方向信息,提高了关联和跟踪的鲁棒性.将无人机航拍图像序列中的地面坦克作为目标进行实验,结果表明本文算法可以实现对多个机动目标的自主探测和跟踪,并具有较好的跟踪鲁棒性.     

Cataglyphis: An autonomous sample return rover

This paper presents the design of Cataglyphis, a research rover that won the NASA Sample Return Robot Centennial Challenge in 2015. During the challenge, Cataglyphis was the only robot that was able to autonomously find, retrieve, and return multiple types of samples in a large natural environment without using Earth‐specific sensors such as GPS and magnetic compasses. It navigates through a fusion of measurements collected from inertial sensors, wheel encoders, a nodding Lidar, a set of ranging radios, a camera on a panning platform, and a sun sensor. In addition to visual detection of a homing beacon, computer vision algorithms provide the sample detection, identification, and localization capabilities, with low false positive and false negative rates demonstrated during the competition. The mission planning and control software enables robot behaviors, determines sequences of actions, and helps the robot to recover from various failure conditions. A compliant, under‐actuated manipulator conforms to the natural terrain before picking up samples of various size, weight, and shape.