Control of multivehicle systems in the presence of uncertain dynamics

In this paper, we present a cooperative control architecture for high-order multivehicle systems having non-identical nonlinear uncertain dynamics. The proposed methodology consists of a local cooperative controller and a vehicle-level controller for each vehicle. The former controller receives the relative output measurements of the neighbouring vehicles in order to solve a containment problem formulated on a leader–follower framework. Specifically, the leaders generate trajectories in which the vehicles (followers) converge to the convex hull formed by those of the leaders. For a special case with one leader, this controller synchronises the output of the vehicles with the output of the leader. The latter controller receives the internal-state measurements for suppressing the nonlinear uncertain dynamics of the vehicle by using a decentralised adaptive control approach. The interaction topology between vehicles is described by undirected graphs and extensions to directed graphs are further discussed. The stability and convergence properties of the proposed cooperative control architecture are analysed by using the results from linear algebra and the Lyapunov theory. Several numerical examples are provided to demonstrate the efficacy of the proposed cooperative control architecture.     

一种基于合作博弈论的自动驾驶车道变更模型

提出一个新颖的车道变更模型,采用合作博弈方法激励车辆参与合作.首次将合作博弈理论应用到车道变更领域,设计用于两车变道的纳什讨价还价变道模型,然后扩展为三车的合作博弈变道模型,并求出变道模型的纳什讨价还价解和夏普利值.为了进一步激励车辆参与合作,在收益分配方案中加入支付补偿部分来实现整体收益的可转移性,从而取得模型的解....     

多无人水面艇协同海上溢油羽流监测

本文讨论多无人水面艇协同海上溢油污染羽流监测问题,其中羽流扩张过程是由二维空间对流–扩散方程描述.本文的主要目标是为水面无人艇构建基于领导–跟随–锚的协同控制算法,实现其对羽流边界的动态合围与实时跟踪,其中羽流边界是一个预先设定阈值的水平集合.为此,选择两艘无人水面艇分别将其设定为边界领导艇和边界锚艇,并将其余艇设定为...     

基于协助车组的车联网多信道协助下载方法

针对目前车联网中单信道协助下载方法的吞吐量低和通信域叠加的问题,提出一种基于对向协助车车组的车联网多信道协助下载方法McDvg。此方法利用对向协助车车组相遇概率大、通信多次重传以及延长数据传输时间的特点在局部范围内形成车组,并设计选车策略选择一组合适的对向车辆携带用户所需的数据为目标车辆提供协助下载;利用对向协助车组可有效延长目标车的协助下载时间,并采用多信道的策略解决单信道情况下的通信冲突问题。最后本文通过相应的仿真实验对该方案进行验证,仿真结果表明该方法可以在高速移动环境中显著提高下载吞吐量,降低盲区的通信时延,从而为当前的车联网提供有效的服务支撑。     

带有激励机制的车联网合作下载信任管理方案

由于高速公路上行驶的车辆间未预先建立信任关系,车辆相互进行合作下载服务时,存在信任危机及安全隐患,严重影响合作下载的发展.为实现可信任的安全合作下载服务,提出基于S O RT算法的信任管理方案,通过服务信任值选择合适的车辆进行合作,设计激励机制和公平交易协议保证合作下载的积极性和公平性,应用区块链技术对车辆的身份隐私以...     

Cooperative vehicle-infrastructure localization based on the symmetric measurement equation filter

Precise and accurate localization is important for safe autonomous driving. Given a traffic scenario which has multiple vehicles equipped with internal sensors for self-localization, and external sensors from the infrastructure for vehicle localization, vehicle-infrastructure communication can be used to improve the accuracy and precision of localization. However, as the number of vehicles in a scenario increases, associating measurement data with the correct source becomes increasingly challenging. We propose a solution utilizing the symmetric measurement equation filter (SME) for cooperative localization to address data association issue, as it does not require an enumeration of measurement-to-target associations. The principal idea is to define a symmetrical transformation which maps measurements to a homogeneous function, thereby effectively addressing several challenges in vehicle-infrastructure scenarios such as data association, bandwidth limitations and registration/configuration of the external sensor. To the best of our knowledge, the proposed solution is among the first to address all these issues of cooperative localization simultaneously, by utilizing the topology information of the vehicles.     

Cooperative autonomous driving: intelligent vehicles sharing city roads

The paper presents the Intelligent Control System Laboratory's (ICSL) Cooperative Autonomous Mobile Robot technologies and their application to intelligent vehicles for cities. The deployed decision and control algorithms made the road-scaled vehicles capable of undertaking cooperative autonomous maneuvers. Because the focus of ICSL's research is in decision and control algorithms, it is therefore reasonable to consider replacing or upgrading the sensors used with more recent road sensory concepts as produced by other research groups. While substantial progress has been made, there are still some issues that need to be addressed such as: decision and control algorithms for navigating roundabouts, real-time integration of all data, and decision-making algorithms to enable intelligent vehicles to choose the driving maneuver as they go. With continued research, it is feasible that cooperative autonomous vehicles will coexist alongside human drivers in the not-too-distant future.     

Autonomous UAV path planning and estimation

Unmanned aerial vehicles (UAVs) have shown promise in recent years for autonomous sensing. UAVs systems have been proposed for a wide range of applications such as mapping, surveillance, search, and tracking operations. The recent availability of low-cost UAVs suggests the use of teams of vehicles to perform sensing tasks. To leverage the capabilities of a team of vehicles, efficient methods of decentralized sensing and cooperative path planning are necessary. The goal of this work is to examine practical control strategies for a team of fixed-wing vehicles performing cooperative sensing. We seek to develop decentralized, autonomous control strategies that can account for a wide variety of sensing missions. Sensing goals are posed from an information theoretic standpoint to design strategies that explicitly minimize uncertainty. This work proposes a tightly coupled approach, in which sensor models and estimation objectives are used online for path planning.     

On‐line estimation and path planning for multiple vehicles in an uncertain environment

A unified approach to cooperative target tracking and path planning for multiple vehicles is presented. All vehicles, friendly and adversarial, are assumed to be aircraft. Unlike the typical target tracking problem that uses the linear state and nonlinear output dynamics, a set of aircraft nonlinear dynamics is used in this work. Target state information is estimated in order to integrate into a path planning framework. The objective is to fly from a start point to a goal in a highly dynamic, uncertain environment with multiple friendly and adversarial vehicles, without collision. The estimation architecture proposed is consistent with most path planning methods. Here, the path planning approach is based on evolutionary computation technique which is then combined with a nonlinear extended set membership filter in order to demonstrate a unified approach. A cooperative estimation approach among friendly vehicles is shown to improve speed and routing of the path. Copyright © 2004 John Wiley & Sons, Ltd.     

一种双无人机协同跟踪地面移动目标方法

针对通信延时情况下双无人机协同跟踪地面移动目标问题进行研究, 构建了基于分布式遗传算法和滚动时域优化结合的目标跟踪航迹规划算法模型。考虑到通信延时会增加目标状态信息数据融合时的误差, 导致无人机跟踪任务效果变差, 结合递推最小二乘滤波和加权最小二乘估计设计了融合方法, 来融合处理目标状态信息; 考虑到无人机对目标的观测效果与未来时刻的目标状态信息密切相关, 采用递推最小二乘滤波预测目标的状态信息, 结合分布式遗传算法和滚动时域优化设计了双无人机目标跟踪航迹规划算法。适应度函数考虑了无人机和目标之间的距离、无人机之间的通信距离、无人机之间的通信角度。仿真结果表明：该协同跟踪方法能够较好地完成跟踪任务; 与一架无人机跟踪相比误差明显减小, 并且可以减小通信延时带来的跟踪误差。     

Distributed vision-aided cooperative localization and navigation based on three-view geometry

This paper presents a new method for distributed vision-aided cooperative localization and navigation for multiple inter-communicating autonomous vehicles based on three-view geometry constraints. Each vehicle is equipped with a standard inertial navigation system and an on-board camera only. In contrast to the traditional approach for cooperative localization, which is based on relative pose measurements, the proposed method formulates a measurement whenever the same scene is observed by different vehicles. Each such measurement is comprising of three images, which are not necessarily captured at the same time. The captured images, to which some navigation parameters are attached, are stored in repositories by some of the vehicles in the group. A graph-based approach is applied for calculating the correlation terms between the navigation parameters associated to images participating in the same measurement. The proposed method is examined using a statistical simulation and is further validated in an experiment that involved two vehicles in a holding pattern scenario. The experiments show that the cooperative three-view-based vision-aided navigation may considerably improve the performance of an inferior INS.     

一种提高系统吞吐量的协助下载补偿模型

随着人们对互联网依赖性的日益提高,人们希望随时随地能够接入Internet,即使在行驶中的汽车里.相比于3G或4G网络,通过路边接入点(AP)接入互联网具有高带宽低延迟的特点.利用经过的车辆携带用户所需要的数据可以有效地减少由于AP分布稀疏所引起的间歇性连接的影响.然而高速行驶的节点,快速的拓扑变化以及传输碰撞域的重叠将导致协助车所携带的数据不能完全地传递给用户,从而降低了系统的吞吐量.文中在先前工作的基础上提出了一种提高系统吞吐量的协助下载补偿模型.在高速公路场景下,协助车在与同向和对向的其它车辆相遇时根据速度和位置信息预测其与目标车相遇的概率,并选取满足条件的车辆备份携带的数据以弥补因传输碰撞域重叠等因素所引起的部分数据包传送失败的损失,从而达到有效地利用盲区(Dark Area)延伸用户下载区域的目的.实验结果表明该方法显著地提高了下载的吞吐量,减少间歇性连接带来的影响.     

Decision-theoretic cooperative sensor planning

This paper describes a decision-theoretic approach to cooperative sensor planning between multiple autonomous vehicles executing a military mission. For this autonomous vehicle application, intelligent cooperative reasoning must be used to select optimal vehicle viewing locations and select optimal camera pan and tilt angles throughout the mission. Decisions are made in such a way as to maximize the value of information gained by the sensors while maintaining vehicle stealth. Because the mission involves multiple vehicles, cooperation can be used to balance the work load and to increase information gain. This paper presents the theoretical foundations of our cooperative sensor planning research and describes the application of these techniques to ARPA's Unmanned Ground Vehicle program     

Cooperative controllers for highways based on human experience

The AUTOPIA program has been working on the development of intelligent autonomous vehicles for the last 10 years. Its latest advances have focused on the development of cooperative man?uvres based on communications involving several vehicles. However, so far, these man?uvres have been tested only on private tracks that emulate urban environments. The first experiments with autonomous vehicles on real highways, in the framework of the grand cooperative driving challenge (GCDC) where several vehicles had to cooperate in order to perform cooperative adaptive cruise control (CACC), are described. In this context, the main challenge was to translate, through fuzzy controllers, human driver experience to these scenarios. This communication describes the experiences deriving from this competition, specifically that concerning the controller and the system implemented in a Citröen C3.     

空地正交视角下的多机器人协同定位及融合建图

针对单一机器人在复杂场景下进行同步定位与建图存在的视角局限等问题,本文提出了一种空地正交视角下的空中无人机与地面机器人协同定位与融合建图方法.鉴于无人机的空中视角与地面机器人视角属于正交关系,该方法主要思想是解决空地正交视角的坐标系转换问题.首先,设计了一种空中无人机和地面机器人协同定位与建图的框架,通过无人机提供的全局俯视图像与地面机器人的局部平视图像获得全面丰富的场景信息.在此基础上,通过融合惯性测量单元和图像信息修正偏移并优化轨迹,利用地面机器人上带有尺度信息的视觉标识,获得坐标系转换矩阵以融合地图.最后多组真实场景实验验证了该方法具有有效性,是空地协同多机器人协同定位及融合建图(simultaneous localization and mapping, SLAM)领域中值得参考的方法.     

汽车转向/防抱死制动协同控制

为了解决汽车转向过程中防抱死制动稳定性问题,提出一种新的协同控制系统.该协同控制结构由转向控制器和制动控制器组成.在转向控制中设计滑模鲁棒自适应控制器和横摆力矩控制器力求改善汽车动态响应,鲁棒自适应性和稳定性.此外定义协同误差,建立汽车协同误差模型并设计汽车防抱死制动鲁棒自适应控制系统.为了减少转向系统和制动系统之间的补偿控制律难以确定的困难,提出耦合误差补偿原理与同一给定控制相结合的新的耦合控制策略.最后用仿真结果验证所设计控制算法的有效性.     

复杂环境下多无人机协作式地面移动目标跟踪

针对多无人机(UAV)协同地面移动目标跟踪问题展开研究.提出一种基于主动感知的问题求解框架,建立多UAV协同目标跟踪问题模型;在此基础上,采用分布式无色信息滤波实现目标状态融合估计与预测;然后,基于预测目标状态,结合滚动时域控制与遗传算法设计一种多UAV在线协同航迹规划算法.仿真结果表明:结合预测目标状态在线优化UAV...     

Traffic congestion detection in large-scale scenarios using vehicle-to-vehicle communications

Cooperative vehicular systems are currently being investigated to design innovative ITS (Intelligent Transportation Systems) solutions for road traffic management and safety. Through the wireless exchange of information between vehicles, and between vehicles and infrastructure nodes, cooperative systems can support novel decentralized strategies for ubiquitous and more cost-attractive traffic monitoring. In this context, this paper presents and evaluates CoTEC (COperative Traffic congestion detECtion), a novel cooperative technique based on Vehicle-to-Vehicle (V2V) communications designed to detect road traffic congestion. CoTEC is evaluated under large-scale highway scenarios using iTETRIS, a unique open source simulation platform created to investigate the impact of cooperative vehicular systems. The obtained results demonstrate CoTEC's capability to accurately detect and characterize road traffic congestion conditions under different traffic scenarios and V2V penetration rates. In particular, CoTEC results in congestion detection probabilities higher than 90%. These results are obtained without overloading the cooperative communications channel. In fact, CoTEC reduces the communications overhead needed to detect road traffic congestions compared to related techniques by 88%.     

Semi‐decentralized nonlinear cooperative control strategies for a network of heterogeneous autonomous underwater vehicles

In this paper, we develop nonlinear distributed or semi‐decentralized cooperative control schemes for a team of heterogeneous autonomous underwater vehicles (AUVs). The objective is to have the network of AUVs follow a desired trajectory, while the agents maintain a desired formation when there is a virtual leader whose position information is only available and known to a very small subset of the agents. The virtual leader does not receive any feedback and information from the other agents and the agents only communicate with their nearest neighboring agents. It is assumed that the model parameters associated with each vehicle/agent is different, although the order of the agents is the same. The developed and proposed nonlinear distributed cooperative control schemes are based on the dynamic surface control methodology for a network of heterogeneous autonomous vehicles with uncertainties. The development and investigation of the dynamic surface control methodology for a team of cooperative heterogenous multi‐agent nonlinear systems is accomplished for the first time in the literature. Simulation results corresponding to a team of six AUVs are provided to demonstrate and illustrate the advantages and superiority of our proposed cooperative control strategies as compared to the methods that are available in the literature. Copyright © 2016 John Wiley & Sons, Ltd.