分布式视觉机器人导航中的定位算法研究

生物启发的无线复眼导航技术是新型的机器人导航方案,将分布在环境中的分布式智能代替了传统的集中式智能。蒙特卡洛定位是近来流行的机器人自主定位算法,将这种算法应用在分布式视觉传感器机器人的定位中,并针对多视觉传感器观测值的最优选择,提出了一种分布式的基于熵的观测量选择方法,目的是选择那些对提高定位精度更有效的观测信息,在保证定位精度的前提下,提高了定位的卖时性和可靠性。仿真实验结果证明了这种算法的可行性。     

多传感器信息融合在移动机器人定位中的应用

机器人自定位是实现自主导航的关键问题之一。为了满足机器人在导航时精确定位的要求,提出一种基于多传感器信息融合的自定位算法。根据对机器人运动机构的分析和运动机构间的刚体约束,建立起机器人的运动学模型;由传感器的工作原理建立里程计和超声波传感器的观测模型;利用扩展卡尔曼滤波(EKF)算法将里程计和超声波传感器采集的数据进行融合;最后,由匹配的环境特征对机器人的位置进行修正,得到精确的位置估计。实验结果表明:该算法明显地消除了里程计的累计误差,有效地提高了定位精度。     

无线传感器环境下粒子群优化的多机器人协同定位研究

协同定位是多机器人自主行为的一项重要技术,重点描述了无线传感器网络环境下结合粒子群优化提出多机器人协同定位算法。该算法引入重采样,解决了粒子耗尽问题,扩大了解空间的范围,保证了种群的多样性,并且引入了惯性权重解决了粒子退化的问题。仿真结果表明,利用无线传感器网络进行辅助导航,采用粒子群优化算法,综合无线传感器网络进行辅助导航,融合各个机器人观测信息,可以降低求解问题的空间维数,在高斯噪声下能有效提高移动机器人定位精度。     

一种基于分布式EKF的多机器人协同定位方法

研究多机器人编队的导航问题,针对多机器人传感器带来的噪声信号影响定位精度,为了提高系统定位精度,提出了一种基于分布式扩展卡尔曼滤波(DEKF)的多机器人协同定位方法.根据航位推算建立单机器人跟踪目标的定位模型后建立编队多机器人的协同定位模型,利用量测信息,通过扩展卡尔曼滤波(EKF)估计各编队机器人局部定位信息,将所得的局部估计值利用优化的融合规则进行处理,得到编队中各机器人的定位数据.通过对单机器人和编队多机器人协同定位进行仿真,结果表明,编队机器人能够利用协同定位方法进行实时定位,且具有更高的定位精度.     

视觉SLAM研究进展

视觉SLAM是指相机作为传感器进行自身定位同步创建环境地图。SLAM在机器人、无人机和无人车导航中具有重要作用,定位精度会影响避障精度,地图构建质量直接影响后续路径规划等算法的性能,是智能移动体应用的核心算法。本文介绍主流的视觉SLAM系统架构,包括几种最常见的视觉传感器,以及前端的功能和基于优化的后端。并根据视觉SLAM系统的度量地图的种类不同将视觉SLAM分为稀疏视觉SLAM、半稠密视觉SLAM和稠密视觉SLAM 3种,分别介绍其标志性成果和研究进展,提出视觉SLAM目前存在的问题以及未来可能的发展。     

基于SLAM算法和深度神经网络的语义地图构建研究

语义地图在移动机器人的导航等任务中有着关键的作用。目前基于视觉的机器人自动定位和制图(SLAM)系统已经能够达到较高的定位精度要求,但是基于多目几何的视觉SLAM算法并未充分利用空间中丰富的语义信息,地图中保留的地图点信息只是没有语义的空间几何点。由于基于卷积神经网络的算法在目标检测领域取得了突破性的成绩,利用目前最新的基于卷积神经网络的目标检测算法YOLO,实现场景的实时目标检测,并结合SLAM算法构建语义地图。将视觉SLAM定位的精确性和深度神经网络在语义提取方面的优势相结合,既能够提高SLAM算法的准确性,同时采集到的丰富图像信息又能进一步训练更加深的神经网络。该算法能够应用于机器人的智能导航,同时也能作为数据采集器为其他视觉任务提供具备语义与几何信息的图像数据。     

WSNs环境下基于高斯混合容积卡尔曼滤波的移动机器人定位算法

针对移动机器人的定位问题,提出一种面向无线传感器网络WSNs( Wireless Sensor Networks)环境下,结合高斯混合容积卡尔曼滤波( GM￣CKF)优化的定位算法。将WSNs对移动机器人的观测、机器人自身对环境特征的观测以及机器人自身运动控制量进行数据融合,并利用带有门限判别和选择性高斯分割的GM￣CKF算法,对机器人的预估位置实施预测修正,降低计算求解的空间维数,提高定位精度。仿真实验结果表明,所提出的方法比传统机器人自定位法定位精度有所提高,算法精度较标准的CKF算法提高了39.11％,比EKF算法提高了65.81％。     

激光测距在移动机器人自主导航中的应用

移动机器人在运动范围内需要有足够的传感器信息可供利用来进行自主导航,在完全未知的环境中,由机器人依靠其自身携带的传感器所提供的信息建立环境模型是机器人进行自主定位和导航的前提之一。介绍了激光测距在移动机器人自主导航中的应用研究;通过二维测距传感器对其周围环境进行扫描,提出了自主导航中地图创建、定位如何用二维扫描获得三维数据流的算法描述,并实验验证该算法的运用使机器人获得一个很好的三维视觉结构图。     

低成本激光和视觉相结合的同步定位与建图研究

激光雷达和视觉传感是目前两种主要的服务机器人定位与导航技术,但现有的低成本激光雷 达定位精度较低且无法实现大范围闭环检测,而单独采用视觉手段构建的特征地图又不适用于导航应用。因此,该文以配备低成本激光雷达与视觉传感器的室内机器人为研究对象,提出了一种激光和视觉相结合的定位与导航建图方法：通过融合激光点云数据与图像特征点数据,采用基于稀疏姿态调整的优化方法,对机器人位姿进行优化。同时,采用基于视觉特征的词袋模型进行闭环检测,并进一步优化基于激光点云的栅格地图。真实场景下的实验结果表明,相比于单一的激光或视觉定位建图方 法,基于多传感器数据融合的方法定位精度更高,并有效地解决了闭环检测问题。     

运用异质传感器信息融合的移动机器人自定位

采用单类、单一传感器很难获得移动机器人的准确定位.为此,运用异质传感器信息融合来提高定位精度.首先,建立机器人运动方程和CCD摄像机观测模型.然后,利用扩展卡尔曼滤波器进行状态估计,选择Q,R矩阵抑制系统的模型噪声和量测噪声,并实现移动机器人的自定位.接着,建立超声波传感器的观测模型,获得机器人的自定位信息.最后,运用BP神经网络,将两种自定位信息进行融合,实现两类传感器的优缺点互补.仿真实验表明,运用异质传感器信息融合能明显地提高移动机器人的自定位精度.     

Reliable Monte Carlo localization for mobile robots

Reliability is a key factor for realizing safety guarantee of fully autonomous robot systems. In this paper, we focus on reliability in mobile robot localization. Monte Carlo localization (MCL) is widely used for mobile robot localization. However, it is still difficult to guarantee its safety because there are no methods determining reliability for MCL estimate. This paper presents a novel localization framework that enables robust localization, reliability estimation, and quick relocalization, simultaneously. The presented method can be implemented using a similar estimation manner to that of MCL. The method can increase localization robustness to environment changes by estimating known and unknown obstacles while performing localization; however, localization failure of course occurs by unanticipated errors. The method also includes a reliability estimation function that enables a robot to know whether localization has failed. Additionally, the method can seamlessly integrate a global localization method via importance sampling. Consequently, quick relocalization from a failure state can be realized while mitigating noisy influence of global localization. We conduct three types of experiments using wheeled mobile robots equipped with a two-dimensional LiDAR. Results show that reliable MCL that performs robust localization, self-failure detection, and quick failure recovery can be realized.     

基于粒子滤波器的移动机器人自定位方法研究

首先,对粒子滤波器的原理进行了简要阐述。然后详细描述了基于粒子滤波器的移动机器人自定位算法——蒙特卡洛定位算法。在ROS(Robot Operating System)平台上对该算法进行了仿真实验并分析了其性能。最后,对蒙特卡洛粒子滤波定位方法用于移动机器人定位进行了总结。结果表明,MCL(蒙特卡洛)算法是一种精确鲁棒的移动机器人概率定位方法,可对解决移动机器人的定位问题提供有意义的参考。提出的机器人自定位方法为机器人在Robocup竞赛中自主执行各种作业提供定位支持,已在2013年中国机器人大赛获奖。     

基于多传感器融合的机器人蒙特-卡洛定位决策

在复杂的不确定环境里,采用单一传感器对机器人进行定位时精度较低,并且易受干扰,可靠性较差。针对这一问题,先将激光测距仪和超声波传感器得到的观测信息利用平方根无迹卡尔曼滤波(SR-UKF)进行融合。根据更新的状态值和误差方差,构造出机器人蒙特—卡洛定位(MCL)的重要性密度函数,充分利用各种传感器采集的冗余信息,综合2种传感器各自的优点。仿真实验表明:基于多传感器融合的机器人蒙特—卡洛定位决策(SR-UKF-MCL)在定位精度和鲁棒性上都有较大的提高,证明了该种方法的可行性。     

Elevation moment of inertia: A new feature for Monte Carlo localization in outdoor environment with elevation map

The elevation map is one of the most popular maps for outdoor navigation. We propose the elevation moment of inertia (EMOI), which represents the distribution of elevation around a robot in an elevation map, for use in the matching of elevation maps. Using this feature, outdoor localization can be performed with an elevation map without external positioning systems. In this research, the Monte Carlo localization (MCL) method is used for outdoor localization, and the conventional method is based on range matching, which compares range sensor data with the range data predicted from an elevation map. Our proposed method is based on EMOI matching. The EMOI around a robot is compared with the EMOIs for all cells of the pregiven reference elevation map to find a robot pose with respect to the reference map. MCL based on EMOI matching is very fast, although its accuracy is slightly lower than that of conventional range matching. To deal with the disadvantage of EMOI matching, an adaptive switching scheme between EMOI matching and range matching was also proposed. Various outdoor experiments indicated that the proposed EMOI significantly reduced the convergence time of MCL. Therefore, the proposed feature is considered to be useful when an elevation map is used for outdoor localization. © 2010 Wiley Periodicals, Inc.     

基于声音的分布式多机器人相对定位

提出了一种基于声音的分布式多机器人相对定位方法.首先,每个机器人通过声源定位算法估计发声机器人在其局部坐标系下的坐标;然后,每个机器人（不含发声机器人）通过无线通信方式将发声机器人在其坐标系下的坐标广播给所有其他机器人,通过坐标变换每个机器人可计算出所有其他机器人在其坐标系下的坐标,从而实现分布式相对定位.理论推导及实验证明只要两个机器人先后发声,通过本文所提方法即可实现多机器人相对定位.室内外环境中采用6个自制小型移动机器人实验表明,所提方法在3米的范围内可实现16厘米的相对定位精度.     

Multirobot Localization in Highly Symmetrical Environments

The paper addresses and solves the problem of multirobot collaborative localization in highly symmetrical 2D environments, such as the ones encountered in logistic applications. Because of the environment symmetry, the most common localization algorithms may fail to provide a correct estimate of the position and orientation of the robot, if its initial position is not known, no specific landmark is introduced, and no absolute information (e.g., GPS) is available: the robot can estimate its position with respect to the walls of the corridor, but it could be critical to determine in which corridor it is actually moving. The proposed algorithm is based upon a particle filter cooperative Monte Carlo Localization (MCL) and implements a three-stage procedure for the global localization and the accurate position tracking of each robot of the team. Online simulations and experimental tests, which investigate different situations with respect to the number of robots involved and their initial positions, show how the proposed solution can lead to the global localization of each robot, with a precision sufficient to be used as starting point for the subsequent robot tracking.     

Effective landmark placement for accurate and reliable mobile robot navigation

Being able to navigate accurately is one of the fundamental capabilities of a mobile robot to effectively execute a variety of tasks including docking, transportation, and manipulation. As real-world environments often contain changing or ambiguous areas, existing features can be insufficient for mobile robots to establish a robust navigation behavior. A popular approach to overcome this problem and to achieve accurate localization is to use artificial landmarks. In this paper, we consider the problem of optimally placing such artificial landmarks for mobile robots that repeatedly have to carry out certain navigation tasks. Our method aims at finding the minimum number of landmarks for which a bound on the maximum deviation of the robot from its desired trajectory can be guaranteed with high confidence. The proposed approach incrementally places landmarks utilizing linearized versions of the system dynamics of the robot, thus allowing for an efficient computation of the deviation guarantee. We evaluate our approach in extensive experiments carried out both in simulations and with real robots. The experiments demonstrate that our method outperforms other approaches and is suitable for long-term operation of mobile robots.     

Hierarchical multi-robot navigation and formation in unknown environments via deep reinforcement learning and distributed optimization

Compared with a single robot, Multi-robot Systems (MRSs) can undertake more challenging tasks in complex scenarios benefiting from the increased transportation capacity and fault tolerance. This paper presents a hierarchical framework for multi-robot navigation and formation in unknown environments with static and dynamic obstacles, where the robots compute and maintain the optimized formation while making progress to the target together. In the proposed framework, each single robot is capable of navigating to the global target in unknown environments based on its local perception, and only limited communication among robots is required to obtain the optimal formation. Accordingly, three modules are included in this framework. Firstly, we design a learning network based on Deep Deterministic Policy Gradient (DDPG) to address the global navigation task for single robot, which derives end-to-end policies that map the robot’s local perception into its velocity commands. To handle complex obstacle distributions (e.g. narrow/zigzag passage and local minimum) and stabilize the training process, strategies of Curriculum Learning (CL) and Reward Shaping (RS) are combined. Secondly, for an expected formation, its real-time configuration is optimized by a distributed optimization. This configuration considers surrounding obstacles and current formation status, and provides each robot with its formation target. Finally, a velocity adjustment method considering the robot kinematics is designed which adjusts the navigation velocity of each robot according to its formation target, making all the robots navigate to their targets while maintaining the expected formation. This framework allows for formation online reconfiguration and is scalable with the number of robots. Extensive simulations and 3-D evaluations verify that our method can navigate the MRS in unknown environments while maintaining the optimal formation.     

采用双重采样的移动机器人Monte Carlo定位方法

移动机器人Monte Carlo定位效率受限于大量粒子的权值更新运算. 本文提出一种实现粒子集规模自适应调整的双重采样方法: 第一层基于粒子权重的固定粒子数重采样, 有效减轻粒子权值退化并保证预测阶段粒子多样性; 第二层粒子稀疏化聚合重采样, 基于粒子空间分布合理性将粒子加权聚合, 从而减少参与权值更新粒子数. 该方法通过提高粒子预测能力保证滤波精度, 通过减少权值更新运算提高了粒子滤波效率. 仿真实验表明, 双重采样方法能够有效实现粒子集规模自适应调整,采用双重采样的移动机器人Monte Carlo定位方法是高效、鲁棒的.