可移动机器人在中心对称环境中的自定位算法

可移动机器人的自定位问题是智能机器人研究中的重要课题，它包含许多传感器技术和定位算法，马尔可夫定位算法的优点是可以使机器人在全局不确定的情况下估计它的位置。这种方法采用概率分布描述机器人的位置信度，机器人通过在运动过程中所获得的传感器数据和运动记录来更新信度分布，然后采用最高信度值来估计它所在的位置。对于只有距离测量传感器的机器人在中心对称环境中仅仅采用马尔可夫自定位法还是无法确定其位置，为了解决中心对称的环境中所存在的问题，建议在机器人上装上陀螺仪或指南针，定义一个角度高斯分布函数，并利用这个函数建立新的机器人感知模型来扩展马尔可夫定位算法，通过仿真程序对多种对称情况进行实验，验证了这一新算法的可行性，这个扩展马尔可夫自定位算法不仅可使机器人在中心对称环境中很快地确定自己的位置，而且可以加快非对称环境中信度分布收敛到真实位置的速度。     

基于机器人听觉的声源定位策略

针对机器人听觉定位,提出了五个传声器组成的阵列作为机器人的耳朵,其中四个传声器组成的平面阵确定声源空间位置,另外一个传声器辅助完成声源位于机器人前后方的判断,并在改进的时延算法上实现声源的空间定位。系统在室内环境下测试,实验结果证明在混响环境下机器人可以实现空间声源定位,该方法具有实时实现的有效性和应用性。     

基于正四棱锥结构的机器人声源定位系统研究

声源定位成为机器人智能研究的重要方向。针对当前声源定位精度不理想、实时性不佳等问题,提出了一种正四棱锥麦克风阵列声源定位结构。采用时间延迟估计的声源定位方法,并提出时延值的快速搜索策略;推导了该结构的基于信号时延的时空映射关系,建立了声源目标位置的几何计算模型,并依据正四棱锥结构特点及冗余的时延值对值域划分,缩小求解范围,运用迭代算法得到声源的位置坐标,并通过双重筛选机制剔除错误的定位结果。实验结果证明了该结构及定位算法在提高系统定位精度和实时性能的有效性,能满足机器人应用中对声源定位的需求。     

A learning particle swarm optimization algorithm for odor source localization

This paper is concerned with the problem of odor source localization using multi-robot system. A learning particle swarm optimization algorithm, which can coordinate a multi-robot system to locate the odor source, is proposed. First, in order to develop the proposed algorithm, a source probability map for a robot is built and updated by using concentration magnitude information, wind information, and swarm information. Based on the source probability map, the new position of the robot can be generated. Second, a distributed coordination architecture, by which the proposed algorithm can run on the multi-robot system, is designed. Specifically, the proposed algorithm is used on the group level to generate a new position for the robot. A consensus algorithm is then adopted on the robot level in order to control the robot to move from the current position to the new position. Finally, the effectiveness of the proposed algorithm is illustrated for the odor source localization problem.     

模糊PID控制下移动机器人定位方法研究

机器人定位研究一直是机器人学研究的重点,但目前机器人定位方法都存在缺点,抗干扰能力差,不能做到准确定位,主要是由于环境等多方面因素的干扰,定位误差会逐渐加大;由于上述原因,提出了一种基于设定值加权模糊PID控制的移动机器人自定位方法;给出了定位过程的参数,为机器人移动建立模型,设计一种模糊 PID 控制器,根据误差及变化率大小,选择模糊定位或PID定位,实现移动机器人的智能定位,提高机器人定位准确的准确性;通过仿真实验结果证明:模糊PID控制的机器人自定位方法对移动机器人的定位过程有较好的改善作用,实用效果较好。     

Position estimation of a mobile robot using images of a moving target in intelligent space with distributed sensors

Latest advances in hardware technology and state-of-the-art of mobile robots and artificial intelligence research can be employed to develop autonomous and distributed monitoring systems. A mobile service robot requires the perception of its present position to co-exist with humans and support humans effectively in populated environments. To realize this, a robot needs to keep track of relevant changes in the environment. This paper proposes localization of a mobile robot using images recognized by distributed intelligent networked devices in intelligent space (ISpace) in order to achieve these goals. This scheme combines data from the observed position, using dead-reckoning sensors, and the estimated position, using images of moving objects, such as a walking human captured by a camera system, to determine the location of a mobile robot. The moving object is assumed to be a point-object and projected onto an image plane to form a geometrical constraint equation that provides position data of the object based on the kinematics of the ISpace. Using the a priori known path of a moving object and a perspective camera model, the geometric constraint equations that represent the relation between image frame coordinates for a moving object and the estimated robot's position are derived. The proposed method utilizes the error between the observed and estimated image coordinates to localize the mobile robot, and the Kalman filtering scheme is used for the estimation of the mobile robot location. The proposed approach is applied for a mobile robot in ISpace to show the reduction of uncertainty in determining the location of a mobile robot, and its performance is verified by computer simulation and experiment.     

无线传感器网络基于移动信标动态选择的定位算法

无线传感器网络节点定位是许多应用的基础.DV-Hop 是一种无需测距的定位算法,但其定位精度依赖于网络的联通状况,对于不规则拓扑的网络定位误差较大.针对这种情况,提出一种新的基于移动信标动态选择的改进 DV-Hop 定位算法,利用一个移动信标在网络中漫游并广播定位分组信息,并在每个虚拟信标中计算当前位置的平均跳距离.未...     

无线传感器网络加权质心相对定位算法

针对基于接收信号强度指示的无线传感器网络加权质心定位算法在实际应用中计算复杂的缺点,提出一种改进型传感器网络加权质心相对定位算法（WCL-RSSI）。该算法主要采用参考节点精选机制和定位组合精选策略选择定位自评误差小的节点进行三边测距定位,以此重建定位权值函数来减小坐标定位误差,最后采用加权质心法计算坐标,并计算该节点的定位自评估误差。仿真实验表明,在同等计算复杂度下,该算法较传统定位方法的定位精度有了明显的提高。     

基于头脑风暴优化算法的多机器人气味源定位

针对现有室内湍流环境下多机器人气味源搜索算法存在历史浓度信息利用率不高、缺少调节全局与局部搜索的机制等问题,提出头脑风暴优化（BSO）算法与逆风搜索结合的多机器人协同搜索算法。首先,将机器人已搜索位置初始化为个体,以机器人位置为中心聚类,有效利用了历史信息的指引作用;然后,将逆风搜索作为个体变异操作,动态调节选中一个类中个体或两个类中个体融合生成新个体的数量,有效调节了全局和局部搜索方式;最后,根据浓度和持久性两个指标对气味源进行确认。在有障碍和无障碍两个环境中将所提算法与三种群体智能多机器人气味源定位算法进行定位对比仿真实验,实验结果表明,所提算法的平均搜索时间减少33%以上,且定位准确率达到100%。该算法能够有效调节机器人全局和局部搜索关系,快速准确定位气味源。     

霍夫空间中多足球机器人协作目标定位算法

针对嵌入式仿人足球机器人提出一种霍夫空间中的多机器人协作目标定位算法。机器人利用实验场地中的标志物采用基于三角几何定位方法进行自定位,把机器人多连杆模型进行简化,通过坐标系位姿变换把图像坐标系转换到世界坐标系中,实现机器人目标定位;在多机器人之间建立ZigBee无线传感器网络进行通信,把多个机器人定位的坐标点进行霍夫变换,在霍夫空间中进行最小二乘法线性拟合,获取最优参数,然后融合改进后的粒子滤波实现对目标小球的跟踪;最后在21自由度的仿人足球机器人上进行仿真和实验。数据结果表明,这种多机器人协作的定位算法的精度提高了约48%,在满足实时性的前提下,对目标的跟踪效果也得到了改善。     

Cooperative localization and evaluation of small-scaled spherical underwater robots

With the goal of supporting localization requirements of our spherical underwater robots, such as multi robot cooperation and intelligent biological surveillance, a cooperative localization system of multi robot was designed and implemented in this study. Given the restrictions presented by the underwater environment and the small-sized spherical robot, an time of flight camera and microelectro mechanical systems (MEMS) sensor information fusion algorithm using coordinate normalization transfer models were adopted to construct the proposed system. To handle the problem of short location distance, limited range under fixed view of camera in the underwater environment, a MEMS inertial sensor was used to obtain the attitude information of robot and expanding the range of underwater visual positioning, the transmission of positioning information could implement through the normalization of absolute coordinate, then the positioning distance increased and realized the localization of multi robot system. Given the environmental disturbances in practical underwater scenarios, the Kalman filter model was used to minimizing the systematic positioning error. Based on the theoretical analysis and calculation, we describe experiments in underwater to evaluate the performance of cooperative localization. The experimental results confirmed the validity of the multi robot cooperative localization system proposed in this paper, and the distance of cooperative localization system proposed in this paper is larger than the visual positioning system we have developed previously.

     

机器人相机空间智能编程方法研究*

针对传统的示教编程方式存在操作复杂,效率低,危险性高等不足,严重限制了工业机器人的推广应用。基于自然的人机交互示教方式,提出了一种基于计算机视觉的相机空间工业机器人智能虚拟编程方法,本方法不需要实际操作示教盒和机器人本体,仅采用辅助示教工具在视觉相机空间示教就实现了工业机器人的虚拟编程。主要研究了实现该方案的关键技术即基于相机空间映射模型的视觉定位技术以及基于K-means聚类算法实现的相机空间映射关系自学习技术。最后,基于自主开发的机器人平台,开展基于相机空间的虚拟智能编程实验,验证了本文提出的相机空间工业机器人智能编程方法的可行性及正确性。     

On generating the motion of industrial robot manipulators

In this study, an intelligent search algorithm is proposed to define the path that leads to the desired position and orientation of an industrial robot׳s manipulator end effector. The search algorithm gradually approaches the desired configuration by selecting and evaluating a number of alternative robot׳s configurations. A grid of the robot׳s alternative configurations is constructed using a set of parameters which are reducing the search space to minimize the computational time. In the evaluation of the alternatives, multiple criteria are used in order for the different requirements to be fulfilled. The alternative configurations are generated with emphasis being given to the robot׳s joints that mainly affect the position of the end effector. Grid resolution and size parameters are set on the basis of the desired output. High resolution is used for a smooth path and lower for a rough estimation, by providing only a number of the intermediate points to the goal position. The path derived is a series of robot configurations. This method provides an inexperienced robot programmer with flexibility to generate automatically a robotic path that would fulfill the desired criteria without having to record intermediate points to the goal position.     

A Localization Algorithm for Autonomous Mobile Robots via a Fuzzy Tuned Extended Kalman Filter

The capability to acquire the position and orientation of an autonomous mobile robot is an important element for achieving specific tasks requiring autonomous exploration of the workplace. In this paper, we present a localization method that is based on a fuzzy tuned extended Kalman filter (FT-EKF) without a priori knowledge of the state noise model. The proposed algorithm is employed in a mobile robot equipped with 16 Polaroid sonar sensors and tested in a structured indoor environment. The state noise model is estimated and adapted by a fuzzy rule-based scheme. The proposed algorithm is compared with other EKF localization methods through simulations and experiments. The simulation and experimental studies demonstrate the improved performance of the proposed FT-EKF localization method over those using the conventional EKF algorithm.     

Hazardous area navigation with temporary beacons

A new area expansion algorithm for the localization scheme, using temporary beacons, is proposed in this paper. The effective area of the active beacons is limited by the strength of the ultrasonic signals in a noisy environment. When a mobile robot needs to move into a hazardous area or into an unstructured environment where the beacons with pre-specified position information are not available, the localization may solely rely on dead reckoning sensors such as encoders. To overcome the error accumulation by using dead-reckoning, a new scheme is developed, in this paper, in which the mobile robot carries a few temporary beacons which do not have any pre-stored position information. When the mobile robot encounters a dangerous or unstructured environment, it utilizes the temporary beacons to localize itself. An auto-calibration algorithm has been developed to provide the position information to the temporary beacons before they are used for the localization. With these temporary beacons and the auto-calibration algorithm, mobile robots can safely pass unstructured areas. The effectiveness of the temporary beacons and auto-calibration algorithm is verified through real experiments of mobile robot navigation.     

A new approach to solve SLAM challenges by relative map filter

In this paper we propose a new approach to solve some challenges in the simultaneous localization and mapping (SLAM) problem based on the relative map filter (RMF). This method assumes that the relative distances between the landmarks of relative map are estimated fully independently. This considerably reduces the computational complexity to average number of landmarks observed in each scan. To solve the ambiguity that may happen in finding the absolute locations of robot and landmarks, we have proposed two separate methods, the lowest position error (LPE) and minimum variance position estimator (MVPE). Another challenge in RMF is data association problem where we also propose an algorithm which works by using motion sensors without engaging in their cumulative error. To apply these methods, we switch successively between the absolute and relative positions of landmarks. Having a sufficient number of landmarks in the environment, our algorithm estimates the positions of robot and landmarks without using motion sensors and kinematics of robot. Motion sensors are only used for data association. The empirical studies on the proposed RMF-SLAM algorithm with the LPE or MVPE methods show a better accuracy in localization of robot and landmarks in comparison with the absolute map filter SLAM.     

可移动机器人的马尔可夫自定位算法研究

马尔可夫定位算法是利用机器人运动环境中的概率密度分布进行定位的方法.使用该 方法机器人可在完全不知道自己位置的情况下通过传感器数据和运动模型来估计自己的位置. 但是,在研究中发现它还存在一些问题,如概率减小到零后就无法恢复.对只有距离传感器的机 器人在对称的环境中仅仅采用该算法就无法确定位置.为了解决这些问题,文中给出了修正算 法,并建议在机器人上装上方向仪(如指南针或陀螺仪等),然后利用定义的一个角度高斯分布 函数来构造新的机器人感知模型.在此基础上详细地阐述了一种新的自定位技术.最后,采用仿 真程序验证了机器人在对称环境中运动时这一新算法的可行性.     

Evolutionary filter for robust mobile robot global localization

Mobile robot global localization aims to determine the robot’s pose in a known environment in the absence of the robot’s initial pose information. This article presents an evolutive localization algorithm known as Evolutive Localization Filter (ELF). Based on evolutionary computation concepts, the proposed algorithm searches stochastically along the state space for the best robot’s pose estimate. The set of pose solutions (the population) represents the most likely areas according to the perception and motion information received. The population evolves by using the observation and motion error derived from the comparison between observed and predicted data obtained from the probabilistic perception and motion model. The resulting global localization module has been integrated successfully in a mobile robot equipped with a laser range finder. Experiments demonstrate the effectiveness, robustness and computational efficiency of the proposed approach.     

基于视觉FastSLAM的移动机器人自主探索方法*

针对室内环境下机器人的移动和定位需要,提出基于视觉FastSLAM的移动机器人自主探索方法.该方法综合考虑信息增益和路径距离,基于边界选取探索位置并规划路径,最大化机器人的自主探索效率,确保探索任务的完整实现.在FastSLAM 2.0的基础上,利用视觉作为观测手段,有效融合全景扫描和地标跟踪方法,提高数据观测效率,并且引入地标视觉特征增强数据关联估计,完成定位和地图绘制.实验表明,文中方法能正确选取最优探索位置并合理规划路径,完成探索任务,并且定位精度和地图绘制精度较高,鲁棒性较好.     

Concurrent localization of multiple robots

A concurrent localization method for multiple robots using ultrasonic beacons is proposed. This method provides a high-accuracy solution using only low-price sensors. To measure the distance of a mobile robot from a beacon at a known position, the mobile robot alerts one beacon to send out an ultrasonic signal to measure the traveling time from the beacon to the mobile robot. When multiple robots requiring localization are moving in the same block, it is necessary to have a schedule to choose the measuring sequence in order to overcome constant ultrasonic signal interference among robots. However, the increased time delay needed to estimate the positions of multiple robots degrades the localization accuracy. To solve this problem, we propose an efficient localization algorithm for multiple robots, where the robots are in groups of one master robot and several slave robots. In this method, when a master robot calls a beacon, all the group robots simultaneously receive an identical ultrasonic signal to estimate their positions. The effectiveness of the proposed algorithm has been verified through experiments.