Towards terrain-aided navigation for underwater robotics

This paper describes an approach to autonomous navigation for an undersea vehicle that uses information from a scanning sonar to generate navigation estimates based on a simultaneous localization and mapping algorithm. Development of low-speed platform models for vehicle control and the theoretical and practical details of mapping and position estimation using sonar are provided. An implementation of these techniques on a small submersible vehicle 'Oberon' are presented.     

基于DDA的碰撞检测及碰撞仿真

碰撞检测是基于物理的动画,计算机辅助没计,计算机辅助制造,计算几何,虚拟现实,机器人等领域必须解决的关键问题,目前仍是研究热点.非连续变形分析方法是一种较新的土木工程领域的数值模拟技术,可以分析不连续块体的运动.将非连续变形分析方法引入到基于物理动厕领域.在计算辅助设计软件设计的虚拟物理场景下,仿真了汽车撞墙和车辆相撞.仿真结果显示:应用非连续变形分析方法可以成功的实现精确的碰撞检测和模拟真实的碰撞响应,在不连续变形、多块体碰撞仿真方面有较大优势.     

A Survey of Scene Understanding by Event Reasoning in Autonomous Driving

Realizing autonomy is a hot research topic for automatic vehicles in recent years. For a long time, most of the efforts to this goal concentrate on understanding the scenes surrounding the ego-vehicle (autonomous vehicle itself). By completing low-level vision tasks, such as detection, tracking and segmentation of the surrounding traffic participants, e.g., pedestrian, cyclists and vehicles, the scenes can be interpreted. However, for an autonomous vehicle, low-level vision tasks are largely insufficient to give help to comprehensive scene understanding. What are and how about the past, the on-going and the future of the scene participants? This deep question actually steers the vehicles towards truly full automation, just like human beings. Based on this thoughtfulness, this paper attempts to investigate the interpretation of traffic scene in autonomous driving from an event reasoning view. To reach this goal, we study the most relevant literatures and the state-of-the-arts on scene representation, event detection and intention prediction in autonomous driving. In addition, we also discuss the open challenges and problems in this field and endeavor to provide possible solutions.     

基于干扰精细估计与神经网络推力分配的载人潜水器控制

针对潜水器在水下运行时会受到洋流、参数摄动等多种干扰因素影响和潜水器的过驱动问题,设计一种基于干扰观测的反步控制器和基于神经网络二次规划的推力分配器的双层控制结构.首先,建立潜水器系统在洋流影响下的动力学模型;其次,将潜水器受到的干扰分为由洋流产生的干扰和由其他因素引起的干扰两部分,分别使用洋流观测器和非线性干扰观测器进行估计,并基于干扰观测信息利用反步法设计运动控制器;然后,针对潜水器的过驱动特性以及推进器的推力受限问题,提出一种基于神经网络二次规划的推力分配方法;最后,使用Matlab进行数值仿真,验证所提控制方法的有效性和优越性.结果表明,基于干扰精细估计与神经网络推力分配的潜水器运动控制系统具有干扰估计更加准确、推进系统的耗能最优,以及避免推进器的推力超限等优势.     

A distributed problem-solving approach to collaborative facility engineering

A framework for collaborative facility engineering is presented. The framework is based on a distributed problem-solving approach to collaborative facility engineering and employs an integration approach called Agent-Based Software Engineering as an implementation vehicle of this approach. The focal entity of this framework is a Multiagent Design Team (MDT) that comprises a collection of software agents (e.g. design software applications with a certain standard communication interface) and a design specialist, which together perform specific design tasks. Multiagent design teams are autonomous and form an organizational structure based on a federation architecture. Every multiagent design team surrenders its autonomy to a system program called facilitator, which coordinates the interaction among software agents in the federation architecture. Facilitators can be viewed as representatives of one or more teams that facilitate the exchange of design information and knowledge in support of the design tasks they perform. In the federation architecture, design specialists collaborate by exchanging design information with others via their software agents, and by identifying and resolving design conflicts by negotiation. In addition to a discussion of the framework's primary components, its realization in an integrated distributed environment for collaborative building engineering is described.     

Expectation-based selective attention for visual monitoring and control of a robot vehicle

Reliable vision-based control of an autonomous vehicle requires the ability to focus attention on the important features in an input scene. Previous work with an autonomous lane following system, ALVINN (Pomerleau, 1993), has yielded good results in uncluttered conditions. This paper presents an artificial neural network based learning approach for handling difficult scenes which will confuse the ALVINN system. This work presents a mechanism for achieving task-specific focus of attention by exploiting temporal coherence. A saliency map, which is based upon a computed expectation of the contents of the inputs in the next time step, indicates which regions of the input retina are important for performing the task. The saliency map can be used to accentuate the features which are important for the task, and de-emphasize those which are not.     

自动驾驶环境下交叉口车辆路径规划与最优控制模型

自动驾驶环境下的交叉口基于车车/车路之间的双向信息交互, 能保障自动驾驶车辆相互穿插与协作地通过交叉口, 而无需信号灯控制. 因此, 如何设计高效的面向自动驾驶车辆通行的交叉口管控模型, 已成为研究的热点. 已有研究在建模时, 均基于自动驾驶车辆在交叉口内部的行驶路径已知并作为模型输入, 且大多对交叉口内部的冲突点进行简化. 本文首先将交叉口空间离散化处理, 考虑车辆的实际尺寸并面向非常规交叉口, 使用椭圆曲线建立转弯车辆行驶路径的精确轨迹方程, 再通过外边界投影降维法建立轨迹方程和交叉口空间的映射关系. 建立了基于混合整数线性规划(Mixed integer linear programming, MILP)的自动驾驶交叉口管控模型, 以交叉口总延误最小为控制目标, 同时优化车辆在交叉口的最佳行驶路径和驶入时刻, 使用AMPL (A mathematical programming language)对模型进行编译并使用CPLEX求解器求解. 与经典感应控制和先到先服务模型进行对比, 结果表明, 本文所提出模型能对车辆进入交叉口的时刻和行驶路径进行双重优化, 显著降低自动驾驶车辆通过交叉口的车均延误, 提高交叉口空间的利用效率.     

A survey of deep learning techniques for autonomous driving

The last decade witnessed increasingly rapid progress in self‐driving vehicle technology, mainly backed up by advances in the area of deep learning and artificial intelligence (AI). The objective of this paper is to survey the current state‐of‐the‐art on deep learning technologies used in autonomous driving. We start by presenting AI‐based self‐driving architectures, convolutional and recurrent neural networks, as well as the deep reinforcement learning paradigm. These methodologies form a base for the surveyed driving scene perception, path planning, behavior arbitration, and motion control algorithms. We investigate both the modular perception‐planning‐action pipeline, where each module is built using deep learning methods, as well as End2End systems, which directly map sensory information to steering commands. Additionally, we tackle current challenges encountered in designing AI architectures for autonomous driving, such as their safety, training data sources, and computational hardware. The comparison presented in this survey helps gain insight into the strengths and limitations of deep learning and AI approaches for autonomous driving and assist with design choices.     

Shared autonomy for low‐cost underwater vehicles

This field report presents an overview of the development and testing of a semi‐autonomous underwater vehicle (sAUV). The work presented here is aimed at bridging the gap between current remotely operated vehicles and autonomous research platforms by developing shared autonomy capabilities for low‐cost underwater vehicles. We use commercially available components and open‐source software interfaces to provide a wider range of capabilities for underwater autonomy research at a lower cost than previously available systems. We describe the overall structure of the system, discuss its capabilities, and provide results demonstrating system performance. We place particular emphasis on shared autonomy, where a human operator is assisted in controlling an underwater tethered vehicle. We present three capabilities developed for the sAUV: (a) an assisted control mode that provides a variable level of assistance using an on‐line estimate of user skill level, (b) a planner to generate paths that avoid tether entanglement, and (c) a sonar processing algorithm that identifies informative sonar images for selecting features for 3D scene reconstruction. The vehicle has been deployed on five off‐shore and near‐shore marine field deployments since 2015, and this report includes selected results from four of those trials to demonstrate the capabilities and limitations of the sAUV system.     

基于Creator的复杂场景建立方法的研究与实现

内含大量设备模型的场景是虚拟现实应用的典型场景.文章阐述了在虚拟场景构造中大规模复杂场景的建模与优化、场景数据库优化、CAD模型显示与优化、数据格式优化等关键技术问题,实现了整个系统的设计与开发.实验证明该系统具有良好的交互性与实时性.     

基于运动模型的道路识别与跟踪算法的研究

自主驾驶与辅助导航是目前国际上研究的热问题,通过对室外行驶车辆上的ＣＣＤ摄像机所采集的长序列立体图象的处理与分析,研究公路汽车自动视觉导航中的道路识别与跟踪问题。     

Overlap Operations and Raster Graphics

This paper outlines the development of a volume and surfaces modelling system for use with raster graphic displays. The ideas are presented in a historical framework to make the reasoning behind the design decisions clear and to indicate the main influences which have guided the work. There are two outcomes of importance: a language form for defining volumes and surfaces; and hardware to convert this representation directly into a display. The hardware provides hidden-line or hidden-area removal as a display primitive which, in an appropriate implementation, can be fast enough to support a range of real-time display applications. Linked to this hidden-line, hidden-area removal facility is an ability to provide interference tests for objects that are being moved around in a scene. Again there appears to be a hierarchy of applications ranging in complexity from interactive volume editing to robot control and vision systems. The new facilities complement existing point and line based display operations, on which most current displays' hardware is based. A good case is emerging for integrating the two approaches into a single hardware implementation for CAD workstations and for simulator display systems.     

Active/dynamic stereo vision

Visual navigation is a challenging issue in automated robot control. In many robot applications, like object manipulation in hazardous environments or autonomous locomotion, it is necessary to automatically detect and avoid obstacles while planning a safe trajectory. In this context the detection of corridors of free space along the robot trajectory is a very important capability which requires nontrivial visual processing. In most cases it is possible to take advantage of the active control of the cameras. In this paper we propose a cooperative schema in which motion and stereo vision are used to infer scene structure and determine free space areas. Binocular disparity, computed on several stereo images over time, is combined with optical flow from the same sequence to obtain a relative-depth map of the scene. Both the time to impact and depth scaled by the distance of the camera from the fixation point in space are considered as good, relative measurements which are based on the viewer, but centered on the environment. The need for calibrated parameters is considerably reduced by using an active control strategy. The cameras track a point in space independently of the robot motion and the full rotation of the head, which includes the unknown robot motion, is derived from binocular image data. The feasibility of the approach in real robotic applications is demonstrated by several experiments performed on real image data acquired from an autonomous vehicle and a prototype camera head     

智能车辆的滑模轨迹跟踪控制

智能车辆是集各种机械装置、传感器、计算机于一体的复杂非线性系统,其轨迹跟踪控制器是研究的关键技术之一。针对高速自主导航智能车辆轨迹跟踪控制器鲁棒性、精确性和实时性的高要求,在智能车辆结构组成与运动模型基础上,设计了一种滑模变结构控制器。通过控制智能车辆的线速度和角速度实现智能车辆对任意路径的跟踪,并用Matlab进行了仿真实验。结果验证了该方法的有效性和可靠性。     

视景仿真系统中动力模型与场景控制的实现

对汽车动力模型的建立与场景控制的设计做了简单的介绍,描述了在视景仿真系统中如何建立汽车动力模型以及对系统中汽车模型的前进、后退、方向转变、遇到雾和阳光的强弱等场景控制的实现。     

Coordinated Control of Networked Vehicles: An Autonomous Underwater System

The specification and design of coordinated control strategies for networked vehicle systems are discussed. The discussion is illustrated with an example of the coordinated operation of two teams of autonomous underwater vehicles collecting data to find the local minimum of a given oceanographic scalar field.     

基于有限时间系统同步的自治水下航行器回收控制

基于主-从系统状态同步的思想,提出了母艇在平面运动中回收自治水下航行器（Autonomous underwater vehicle,AUV）的一种控制方法. 在给出母艇和自治水下航行器的动力学模型基础上,建立了自治水下航行器（从系统）接收母艇（主系统）的状态信息并控制自身接近母艇的主从控制方案,使母艇自主回收水下航行器的问题转化为两者的运动状态同步问题. 利用有限时间稳定性理论,设计了一种在常值海流扰动影响下,自治水下航行器能够在有限时间内被母艇回收的滑模控制器,理论证明和仿真实例证实了该控制器的有效性.     

Secure Mobility and the Autonomous Driver

Autonomous mobility systems developed for unmanned ground vehicles may have additional benefits by enhancing system performance and reducing demands on operators for manned ground vehicles. This effort examines the potential impact of introducing autonomous mobility to control manned vehicles while operators performed secure mobility. Eleven Soldiers participated in an experimental task requiring concurrent control of a manned and an unmanned Stryker performing a road march, scanning of the local environment for targets, and planning of a reconnaissance route for a third simulated asset. The control of the manned vehicle was varied between autonomous and manual control and several speed and accuracy variables were examined for each task. Subjective measures of operator workload, stress, and motion sickness were also examined. The results support the potential benefits of incorporating autonomous mobility into manned platforms. In speed-matched conditions, autonomous mobility was associated with decreased manned vehicle mission time, faster operator reaction times to targets, greater instances of multitasking while under motion, and lower subjective operator workload measures than with manual driving. In conclusion, autonomous mobility technologies have the potential to free up resources from the vehicle operator and allow for better operator performance on tasks other than vehicle control.     

Experimental Verification of a Drift Controller for Autonomous Vehicle Tracking: a Circular Trajectory Using LQR Method

This study develops an autonomous vehicle control method that enables it to perform a drift maneuver which is an expert driving technique consisting of sliding the rear wheel intentionally for fast cornering. By developing an autonomous control algorithm for such an agile maneuver, the safety of the future autonomous vehicle on extreme conditions such as slippery road, will be increased. Drift equilibrium states are derived to find the suitable feedforward control input for the scale car to enter the drifting region. In addition, a feedback controller is designed based on the linear quadratic regulator method in order to track the circular trajectory and maintain drift equilibrium states. To validate the performance of the developed control algorithm a 1:10 scale car experimental platform is developed with on-board control and sensor system. The feasibility of the developed method for the autonomous vehicle is confirmed through both simulation and experiments following circular trajectories while maintaining the desired equilibrium states.

     

自动驾驶交叉路口测试场景建模及验证方法

自动驾驶汽车在缓解交通拥堵和消除交通事故方面发挥着重要作用.为了保证自动驾驶系统的安全性和可靠性,在自动驾驶汽车部署到公共道路之前,必须进行全面的测试.现有的测试场景数据大多来源于交通事故和交通违法场景,而且自动驾驶系统最基本的安全需求就是遵守交通法规,这充分体现了自动驾驶汽车遵守交通规则的重要性.然而,目前严重缺少针对交通法规构建的自动驾驶测试场景.因此,本文从交通法规出发,根据自动驾驶系统安全需求,提出交叉路口测试场景的Petri网建模及形式化验证方法.首先,依据自动驾驶测试场景对交规进行分类,提取适合自动驾驶汽车的文本交规,并进行半形式化表征.其次,以覆盖道路交通安全法规以及测试场景功能测试规程为目标,融合交叉路口场景要素的交互行为,合理选择并组合测试场景要素,布设交叉路口测试场景.然后,基于交规的测试场景被建模为一个Petri网,其中,库所描述自动驾驶汽车的状态,变迁表示状态的触发条件,并选择时钟约束规范语言（CCSL）作为中间语义语言,将Petri网转换为一个可进行形式化验证的中间语义模型,提出具体的转换方法.最后,通过Tina软件分析验证交规场景模型的活性、有界性和可达性,结果表明所建模型的正确性,并基于SMT的分析工具MyCCSL来分析CCSL约束,采用LTL公式以形式化方法验证交规场景模型的一致性.