全自主移动机器人在未知环境中的运动规划

针对机器人在未知环境中的运动规划,本文给出了一种基于激光传感器的完备路径规划算法,使机器人可以有效地扫描整个未知环境。使得机器人扫描整个未知环境的行走路径较短,所需的时间也较短。计算机仿真验证了算法的可行性,同时体现了算法的优越性。     

多信息融合的目标辨识研究

针对智能移动机器人未知环境探测与地图创建的研究与应用,在优化图像匹配算法的基础上,提出了一种基于图像信息与激光测距信息融合的算法,用于机器人作业环境中进行目标有效辨识,以完成机器人的环境探测与地图创建。实验结果证明了该算法的可行性和有效性。     

移动机器人辅助下基于GM-CKF的无线传感器网络节点定位研究

针对无线传感器网络(WSNs)节点定位问题,提出一种移动机器人辅助作用下,融入高斯混合容积卡尔曼滤波(GM-CKF)优化的节点定位方法。将移动机器人与WSNs结合,发挥两者的特点和优势,充分利用机器人的机动性及无线传感器节点的可计算性,设计并仿真了一种机器人-节点、节点-节点协作的节点定位方式,并利用带有门限判别和选择性高斯分割的GM-CKF算法,对目标节点的预估位置实施预测修正。仿真结果表明,所提出的移动机器人与WSNs协作定位方法实现了对节点的定位估计,GM-CKF算法的融合有效提高了定位的精度和稳定性。     

融合行人运动信息的室内移动机器人动态避障方法

为了提高移动机器人在室内人机共融环境下的运动安全和交互性,提出了一种融合行人运动信息的室内移动机器人动 态避障方法,同时考虑任务约束和社会规则。 首先,利用 YOLO v3 算法和 Deep Sort 算法分别对室内环境中的行人进行实时检 测与目标跟踪,计算行人在过去时刻的历史轨迹。 然后,利用 Social-GAN 算法构建行人交互模型,实现轨迹预测。 在此基础上, 将行人的运动状态融合进机器人避障算法之中,根据社会规则设计评价函数,对机器人采样速度样本进行评估,使移动机器人 能够以安全和舒适的方式绕过行人,确保室内人机共融环境下移动机器人的社会接受性。 通过实验对比分析,与传统 DWA 方 法相比,本文方法不仅可以提高机器人导航避障效率,在相同室内场景下导航避障时间由 23. 56 s 提高到 19. 38 s,而且可以有 效降低与行人发生碰撞的风险,保证机器人导航的安全和社交性。     

基于无线传感网络的移动机器人系统设计

研究了一种基于无线传感网络的移动机器人系统设计方法,在对各个功能模块分析的基础上,设计了移动机器人的避障控制系统,利用多传感器信息融合技术,有效提高了控制系统精度.该移动机器人系统在无线传感网络环境下进行了相关避障实验,实验结果表明,系统设计合理、性能优良、算法适用、可靠性高,能实现预期的实验效果.     

基于粒子滤波的运动机器人目标定位

机器人完成各种应用的前提是准确获知自身及运动目标的相对位置,由于机器人在运动控制的过程中自身携带的传感器获取的位置和角度信息存在误差,会导致移动机器人在目标定位过程中出现误差。为提高定位的准确性,提出了基于相对定位的方法,建立目标运动的相对运动模型,并基于观测距离和角度的测量方程运用粒子滤波方法对运动目标进行定位,实验与仿真结果表明,在不同强度的非高斯噪声影响下,粒子滤波算法都能够有效的对其进行定位,且具良好的精度。     

基于SR-CKF的移动机器人动态目标跟踪算法

针对移动机器人在未知复杂环境中动态目标跟踪存在的数值不稳定、计算量大和精度较差等问题,提出基于平方根容积卡尔曼滤波的移动机器人动态目标跟踪算法(SR-CKF-SLAM-OT)。该算法的系统状态由地图环境特征、机器人和目标作为一个整体构成。建立目标和机器人的动态模型进行预测、数据关联和更新,在更新过程中直接传递目标状态均值和协方差矩阵的平方根因子,降低了计算的复杂度。此外,通过数据关联环节能够有效的降低伪观测值对系统状态估计的影响。仿真结果表明:相比基于EKF的动态目标跟踪算法,所提出的动态目标跟踪算法目标和机器人均方根误差分别降低了36.3%和38.2%,SR-CKF-SLAM-OT算法有效地满足了移动机器人动态目标跟踪的需求。     

多传感器融合的移动机器人室外激光 SLAM 算法优化与系统实现

针对移动机器人室外环境开阔场景大范围建图时,激光雷达里程计位姿计算不准确从而导致 SLAM 算法精度下降等问 题,设计了一种基于多传感器融合的 SLAM 优化算法。 算法上,通过前端里程计优化提升 SLAM 算法的可靠性,将适用于室外 的 GNSS 等传感器信息与激光里程计融合,在技术上实现了扩展卡尔曼滤波的轻量化并将其嵌入于 LOAM 算法架构中,在尽可 能不增加资源负担的情况下对前端里程计进行改进;在优化算法基础上,搭建了实际移动机器人平台并移植算法,实现了可供 参考的多传感器融合硬件方案与扩展卡尔曼滤波在实际工程中处理多传感器数据的方法。 真实场景下的实验结果表明,在增 加了里程计运算量后算法仍能稳定保持 10 Hz 的室外建图,在复杂开阔环境与低成本条件下具有可靠性与可行性。     

目标信号导航的机器人路径二次优化

根据蚂蚁觅食行为的特征,提出多机器人根据目标信号源强度导航对未知环境中已有路径进行优化的方法.多个机器人依赖自身传感器获取局部环境信息,对所有路径点按调整迫切程度优先排序并修改该路径点位置,在总路程长度上达到全局最优的效果.     

基于假设坐标系法的长定子直线同步电机无速度传感器算法

针对长定子直线同步电机的牵引控制提出了一种无速度传感器算法。该算法根据假设坐标系与实际动子坐标系之间的角度偏差来构造扰动观测器来观测假设坐标系中的反电动势值,并利用反电动势通过反三角函数获得角度偏差,通过修正这一偏差来获得动子位置和速度信息。整套算法在IOWorks环境中开发,并在基于VME总线的控制系统中进行实验。实验结果表明本算法能够很好地估算出动子的位置和速度信息。     

一种基于功放幅频特性的信号源设计方法

As unmanned electric wheeled mobile robots have been increasingly applied to high-speed operations in unknown environments, the wheel slip becomes a problem when the robot is either accelerating, decelerating, or turning at high speed. Ignoring the effect of wheel slip may cause the mobile robot to deviate from the desired path. In this paper a recently proposed method is implemented to estimate the surface conditions encountered by an unmanned wheeled mobile robot, without using extra sensors. The method is simple, economical and needs less processing power than for other methods. A reaction torque observer is used to obtain the rolling resistance torque and it is applied to a wheeled mobile robot to obtain the surface condition in real-time for each wheel. The slip information is observed by comparing the reaction torque of each wheel. The obtained slip information is then used to control the torque of both wheels using a torque controller. Wheel slip is minimized by controlling the torque of each wheel. Minimizing the slip improves the ability of the unmanned electric wheeled mobile robot to navigate in the desired path in an unknown environment, regardless of the nature of the surface.     

Illumination Adaptive Identification Algorithm of a Reconfigurable Modular Robot

Reconfigurable modular robots feature high mobility due to their unconstrained connection manners. Inspired by the snake multi-joint crawling principle, a chain-type reconfigurable modular robot (CRMR) is designed, which could reassemble into various configurations through the compound joint motion. Moreover, an illumination adaptive modular robot identification (IAMRI) algorithm is proposed for CRMR. At first, an adaptive threshold is applied to detect oriented FAST features in the robot image. Then, the effective detection of features in non-uniform illumination areas is achieved through an optimized quadtree decomposition method. After matching features, an improved random sample consensus algorithm is employed to eliminate the mismatched features. Finally, the reconfigurable robot module is identified effectively through the perspective transformation. Compared with ORB, MA, Y-ORB, and S-ORB algorithms, the IAMRI algorithm has an improvement of over 11.6% in feature uniformity, and 13.7% in the comprehensive indicator, respectively. The IAMRI algorithm limits the relative error within 2.5 pixels, efficiently completing the CRMR identification under complex environmental changes.     

机器人视觉伺服系统的无源化设计

针对视觉伺服机器人手眼定位问题,采用非线性系统理论中的无源化方法,在不需要物体的深度精确值、几何模型及单应性矩阵的计算情况下,设计了摄像机的平移和旋转速度,同时使用自适应控制方法对深度进行估计;基于图像误差实现伺服控制,确保了系统全局渐近可稳定性,该稳定性条件不依赖摄像机的内参数变化。仿真结果表明,利用本方法所设计的控制器,给出了摄像机的速度（包括平移和旋转速度）,且能够很好地驱使摄像机由任意初始位置向期望位置运动,实现大范围内渐近稳定。     

基于SR-CKF的相对方位多机器人协同定位算法

针对单移动机器人在探索未知复杂环境时,存在鲁棒性较差、效率较低等问题以及现有多机器人协同定位算法实时性较差、数值不稳定和定位精度较差等缺陷,提出基于平方根容积卡尔曼滤波的相对方位多机器人协同定位算法。通过建立机器人运动方程和观测方程,利用相对方位作为测量值,进一步得到多机器人协同定位的动态模型。在更新过程中直接传递目标状态均值和协方差矩阵的平方根因子,降低了计算的复杂度。仿真结果表明:相比基于相对方位的EKF、UKF协同定位算法,本文提出的协同定位算法均方根误差降低了87.04%和52.10%,运行时间比UKF协同定位算法减少了1.45%,表明该算法在协同定位性能上更优越。     

Adaptive relative velocity estimation algorithm for autonomous mobile robots using the measurements on acceleration and relative distance

In this article, an adaptive algorithm is proposed for online velocity estimation of the autonomous mobile robots (AMRs) without positioning data received from a Global Positioning System (GPS) module or other means for odometry. Unlike the popular Kalman and particle filters that use the measurements on vectors of global (or local) position and acceleration of a mobile robot, the proposed adaptive relative velocity estimation (ARVE) algorithm requires the scalar value of measured distance to a beacon agent and also the measurement on acceleration vector, in order to generate an online estimation of the global velocity vector of a mobile robot. Combining the ARVE algorithm with the recently proposed adaptive relative position estimation (ARPE) algorithm provides a solution for online estimation of the translational states of a mobile robot without accessing the GPS data, which makes the package applicable in both indoor and outdoor environments. The stability of the ARVE algorithm is analyzed with LaSalle-Yoshizawa theorem. In addition, two simulation studies are provided to show the application of the proposed estimation package (ARVE+ARPE) for aerial AMRs in two cases corresponding to the stationary and moving beacon agents. In the simulation results, it is shown that the estimation package can be used in conjunction with the recently proposed adaptive model-free control (AMFC) algorithm to achieve desired tracking objective in autonomous movement of a quadrotor, without requiring the information on the internal dynamics of the robot.     

An approach to absolute position control based on object coordinate

This paper describes accurate position control in an object coordinate system. When motion control of an industrial robot placed in a global coordinate system is taken to be in an object coordinate system, it is preferable and convenient to decide its motion by having the operator teach the robot. However, the teaching procedure requires considerable time and effort. Moreover, whenever the relative position between the robot and the object is changed, the operator needs to teach the operation again. To improve the above issue, a strategy that decides the robot motion without the teaching must be developed. This paper proposes a control strategy that does not require teaching and enables the creation of the desired motion without affecting the relative position error between the robot and the target object in object coordinate space defined by a PSD (Position Sensor Detector). In the proposed approach, the estimation algorithm of the kinetic transformation between the global and the object coordinates is introduced by using PSD output, and the error of coordinate transformation estimated by the proposed approach is compensated for in the global coordinate system. The validity of the proposed method is shown using simulations and experiments. © 2006 Wiley Periodicals, Inc. Electr Eng Jpn, 155(3): 53–61, 2006; Published online in Wiley InterScience ( www.interscience.wiley.com ). DOI 10.1002/eej.20354     

Sensorless control of interior permanent-magnet machine drives with zero-phase lag position estimation

This paper presents an improved method to estimate rotor motion states for an interior permanent-magnet machine drive. This approach is based on the estimation of a saliency-based electromotive force (EMF) in the stationary reference frame using a state filter. The spatial information obtained from the estimated saliency-based EMF is used in an observer to estimate the motor motion states. By using the commanded torque as a feedforward input to the observer, the motion state estimation has zero-phase lag, providing a very-high-bandwidth estimate.