一种基于磁传感器的MEMS陀螺标定方法

根据微型航姿测量系统各传感器的特点,研究出了一种基于磁传感器输出的MEMS陀螺标定方法,并根据MEMS陀螺误差参数模型设计相应的补偿算法,分别对MEMS陀螺的零偏和标度因数误差进行了补偿。与传统标定方法相比,该方法实现简单,适用于现场标定。实验结果表明,该标定方法能够有效地提高MEMS陀螺测量精度,补偿后陀螺在静态条件下2分钟内,俯仰角漂移小于0.035°,倾斜角漂移小于0.15°,航向角的漂移小于0.2°。当陀螺三轴均有角速率输入时,在角速度小于25°/s情况下误差都能保持在±2°以内。     

一种自适应残差补偿算法在移动机器人姿态估计中的应用研究

针对两轮移动机器人MEMS IMU姿态估计的数据融合问题,提出一种以卡尔曼滤波为基础的自适应残差补偿算法。该算法结合惯性传感器误差模型与移动机器人姿态模型构建卡尔曼滤波器,利用卡尔曼滤波量测更新的加速度残差自适应补偿非重力载体位移加速度对姿态估计的影响。实验结果表明,该算法有效的融合了MEMS IMU姿态测量数据,抑制了传感器随机漂移误差,同时自适应补偿了非重力载体位移加速度。     

基于Kalman滤波算法的姿态传感器信号融合技术研究

针对应用三轴陀螺仪和三轴加速度传感器的四旋翼飞行器姿态角测量问题,提出了基于Kalman滤波算法的姿态传感器信号融合方法。该方法将陀螺仪输出的角速度误差作为时变误差处理,认为陀螺仪输出的角速度误差与其所测角速度及上一时刻的角速度输出误差相关,并据此建立陀螺仪测量线性方程,在此基础上,应用Kalman滤波算法,以加速度计输出的姿态角对陀螺仪测量的姿态角进行修正,从而达到姿态角准确测量的目的。实验结果表明：应用Kalman滤波算法对加速度传感器和陀螺仪信号融合后可有效消除姿态角测量累积误差并显著改善姿态角测量的动态特性。     

机械臂D-H参数和减速比几何标定及误差补偿

针对机械臂D-H参数和关节电机减速比不精确导致机械臂绝对定位精度降低的问题,提出了在利用几何分析标定机械臂D-H参数的基础上,通过分析关节实际旋转角度和相应电机编码器码值的线性关系,标定关节电机减速比的方法;针对关节角误差微分补偿法计算量大的缺点,通过推导机械臂末端位姿矩阵误差和关节角误差之间的微分关系建立误差模型,求解关节补偿角,避免了雅各比矩阵的求取,提高了计算效率;最后采用三维激光跟踪仪搭建测量系统,完成了一种6自由度机械臂的标定及补偿实验;实验结果表明,通过参数标定及误差补偿,机械臂的绝对定位误差均值从标定前的2.83 mm和1.14°降低到0.54 mm和0.24°,验证了方法的有效性。     

姿态测量单元在船载卫星天线上的应用

提出了在船载卫星天线系统中,一种以姿态测量单元为核心的控制系统方案,详细叙述了该系统的三轴空间转动关系,推导出了如何由姿态测量单元输出的三维方向角进行天线反射面姿态修正的一系列数学表达式,介绍了进行前馈补偿的系统稳定方法;对系统进行摇摆试验测得方位角误差小于1.3°,俯仰角和横摇角误差小于0.5°;福州海关应用证明该系统能满足卫星通信稳定系统的技术要求。     

一种数字磁罗盘全罗差自主优化补偿方法

为了进一步提高数字磁罗盘全姿态罗差补偿精度,提出了一种基于地磁场分量的罗差自主优化补偿方法.从罗差补偿模型出发,分析椭球拟合补偿方法的局限性,在对参数缺失和剩余误差分析的基础上,建立了包含缺失参数的优化补偿模型;针对非线性优化模型引入粒子群算法PSO(Particle Swarm Optimization)对模型参数进行估计,数值仿真结果证明了算法可有效估计缺失参数.实验结果表明,优化补偿过程无需借助外部辅助姿态信息,俯仰角-20°姿态下,优化补偿方法在椭球假设补偿基础上将其最大误差由4.8°降至1.9°,误差标准差由1.5°降至1.1°.     

神经网络补偿算法在基于MEMS的姿态检测中的应用

针对基于微机电传感器的姿态检测领域存在的姿态测量误差问题,为进一步提高姿态检测的精度,提出了一种基于神经网络的姿态估计误差补偿方法。采用开源的微型飞行器在室内环境进行真实飞行实验采集的数据集,借助BP神经网络的非线性映射能力,建立了关于微机电传感器的输出与姿态估计误差之间的姿态误差补偿模型;根据微机电传感器的输出信息,直接预测得到横滚角、俯仰角和偏航角的误差补偿角度。实验结果表明,利用所提出的神经网络进行姿态补偿之后,姿态估计误差大大减小,表明神经网络对于提高姿态检测的精度具有一定的作用。     

多新息抗差-自适应卡尔曼滤波定位算法研究

针对移动机器人在定位过程中,由传感器测量误差和机器人模型引起的位姿误差导致系统定位精度急剧下降的问题,提出了一种多新息卡尔曼滤波算法.在标准卡尔曼滤波的基础上,当传感器测量值存在误差时,引入抗差权因子,通过改变误差测量值的权值提高滤波器的估计精度;当机器人位姿存在误差时,引入自适应因子,通过调整状态协方差矩阵的大小抵制位姿误差引起的滤波发散.同时,引入了多新息,即多个时刻的新息向量,进一步提高此非线性系统的精度.实验表明:当存在测量误差和位姿误差时,该滤波算法能有效提高定位精度.     

基于重力四元数的陀螺漂移估计与补偿

为提高钻探中的钻具姿态测量精度,提出一种基于重力四元数的MEMS惯性随钻姿态测量方法.采用MEMS惯性器件构建钻具姿态测量系统,把加速度计数据解算的姿态四元数作为观测四元数,陀螺仪数据解算的姿态四元数作为误差四元数;然后将陀螺仪漂移融入误差四元数,建立重力四元数估计陀螺仪误差四元数的模型,采用最小二乘法估计陀螺仪三轴漂移,进而补偿陀螺仪姿态四元数;通过补偿后的姿态四元数解算出钻具姿态.最后设计了转台、振动台实验和钻进模拟实验,实验结果表明,姿态四元数补偿后的井斜角和工具面角漂移由平均10 °/h减小到约0.2 °/h,方位角误差由平均12 °/h减小到约0.46 °/h,实现了加速度计补偿陀螺的三轴漂移,表明该方法能够有效提高钻具的姿态测量精度.     

一种新型磁阻传感器的研究及应用

介绍了美国Honeywell公司生产的磁阻弱磁传感器的性能特点,以及由其组成的三轴智能型弱磁传感器在测量磁航向方面的应用.实验结果表明:在捷联状态下,用垂直陀螺提供载体的姿态角,在俯仰角、倾斜角达±45°的情况下,测角精度优于 0.6°.该传感器应用的显著特点是体积小、重量轻.它与垂直陀螺组成简易捷联航姿系统,已用于无人机的控制与导航.     

基于大数据聚类的工业遥操作机器人位姿定位控制系统设计

针对工业遥操作机器人位姿定位过程中难以同步控制位置和姿态角,导致位姿定位准确性较差的问题,利用大数据聚类技术,从硬件和软件两个方面优化设计工业遥操作机器人位姿定位控制系统。通过位姿传感器的改装,保证传感器设备能够同时测量机器人位置与姿态,改装定位控制器和驱动器。在系统硬件的支持下,考虑机器人组成结构、运动原理和动力学理论,构建机器人数学模型,在该模型下模拟机器人遥操作过程,确定机器人位姿的定位控制目标。实时采集机器人位姿数据,利用大数据聚类技术计算定位控制量,在控制器的约束下,实现系统的位姿定位控制功能。通过系统测试实验得出结论：综合多种类型的运动情况,在优化设计系统的控制下,机器人的位置误差平均值为4.5mm,姿态角控制误差为0.04°。     

A non-contact laser speckle sensor for the measurement of robotic tool speed

A non-contact speckle correlation sensor for the measurement of robotic tool speed is described that is capable of measuring the in-plane relative velocities between a robot end-effector and the workplace or other surface. The sensor performance has been assessed in the laboratory with sensor accuracies of ±0.01 mm/s over a ±70 mm/s velocity range. The effect of misalignment of the sensor on the robot was assessed for variation in both working distance and angular alignment with sensor accuracy maintained to within 0.025 mm/s (<0.04%) over a working distance variation of ±5 mm from the sensor design distance and ±0.4 mm/s (0.6%) for a misalignment of 5°. The sensor precision was found to be limited by the peak fitting accuracy used in the signal processing with peak errors of ±0.34 mm/s. Finally an example of the sensor’s application to robotic manufacturing is presented where the sensor was applied to tool speed measurement for path planning in the wire and arc additive manufacturing process using a KUKA KR150 L110/2 industrial robot.     

高速磁浮车间隙传感器齿槽效应补偿方法研究

齿槽效应是高速磁浮车间隙传感器面临的一个特殊性问题。针对齿槽效应带来的齿槽误差问题,提出在传感器探头内布置齿槽位置检测线圈,采用径向基函数(RBF)神经网络建立齿槽效应逆模型,依据位置信号对传感器的输出进行齿槽补偿的方法。仿真结果表明:在2～20 mm范围内补偿器输出最大误差为±0.18 mm,该种方法可以有效地消除齿槽效应,并提高传感器的检测精度,满足高速磁浮车悬浮控制系统要求。     

排爆机器人机械臂定位精度误差自动补偿方法研究

针对于排爆机器人在进行排除爆破物质时,机械臂不能满足绝对准确的定位要求,位置检测精度与实际距离之间存在一定的误差。为了解决这一问题,提出排爆机器人机械臂定位精度误差自动补偿方法。基于D-H运动模型和微分变换法创建排爆机器人机械臂位姿误差模型,对误差模型进行重复参数分析,去除重复参数获得可辨识的线性方程;在可辨识的运动学参数误差模型线性方程中加入一个增量进行误差补偿。最后通过仿真实验结果表明,所提方法通过对机械臂位姿误差模型进行有效补偿,使排爆机器人机械臂绝对定位精度均值提升1.3mm。     

基于T-S模糊神经网络的齿槽效应补偿方法研究*

针对齿槽效应带来的齿槽误差问题,提出在传感器探头内布设齿槽位置检测线圈,建立传感器齿槽特性模型和基于T-S模糊神经网络的齿槽补偿系统模型,依据齿槽位置信号对传感器进行齿槽误差补偿。利用附加动量的BP学习方法对网络进行学习和测试。仿真结果表明补偿模型的输出不再随齿槽位置波动,最大误差为依0.2mm,该种方法可以有效地消除齿槽效应并提高传感器的检测精度,满足高速磁浮车悬浮控制系统要求。     

基于D-H法的农业采摘机器人运动协作控制系统设计

为提升农业采摘机器人运动协作控制性能,降低机器人碰撞概率,利用D-H法优化设计机器人运动协作控制系统。改装位置、力矩以及碰撞传感器设备,优化运动协作控制器与驱动器,调整系统通信模块结构,完成硬件系统的优化。利用D-H法构建农业采摘机器人数学模型,在该模型下,利用传感器设备实现机器人实时位姿的量化描述,通过机器人采摘流程的模拟,分配机器人运动协作任务,从位置和姿态等多个方面,确定运动协作控制目标,经过受力分析求解机器人实际作用力,最终通过控制量的计算,实现农业采摘机器人的运动协作控制功能。通过系统测试实验得出结论：与传统控制系统相比,机器人位置、姿态角和作用力的控制误差分别降低了约40mm、0.2°和1.2N,在优化设计系统控制下,机器人的碰撞次数得到明显降低。     

Magnetic landmark-based position correction technique for mobile robots with hall sensors

We propose a precise position error compensation and low-cost relative localization method in structured environments using magnetic landmarks and hall sensors. The proposed methodology can solve the problem of fine localization as well as global localization by tacking landmarks or by utilizing various patterns of magnetic landmark arrangement. In this paper, we consider two patterns of implanted permanent magnets on the surface, namely, at each vertex of regular triangles or rectangles on a flat surface. We show that the rectangular configuration of the permanent magnetic bars is better for a robust localization under sensor noise. For the experiments, permanent magnet sets in rectangular configuration are placed on the floor as landmarks at regular intervals, and magnetic hall sensors are installed at the bottom of a mobile robot. In our implementation, the accuracy after the error compensation is less than 1 mm in the position and less than 1° in the orientation. Due to the low cost and accuracy of the proposed methodology, it would be one of the practical solutions to the pose error correction of a mobile robot in structured environments.     

A high precision visual localization sensor and its working methodology for an indoor mobile robot

To overcome the shortcomings of existing robot localization sensors, such as low accuracy and poor robustness, a high precision visual localization system based on infrared-reflective artificial markers is designed and illustrated in detail in this paper. First, the hardware system of the localization sensor is developed. Secondly, we design a novel kind of infrared-reflective artificial marker whose characteristics can be extracted by the acquisition and processing of the infrared image. In addition, a confidence calculation method for marker identification is proposed to obtain the probabilistic localization results. Finally, the autonomous localization of the robot is achieved by calculating the relative pose relation between the robot and the artificial marker based on the perspective-3-point (P3P) visual localization algorithm. Numerous experiments and practical applications show that the designed localization sensor system is immune to the interferences of the illumination and observation angle changes. The precision of the sensor is ±1.94 cm for position localization and ±1.64? for angle localization. Therefore, it satisfies perfectly the requirements of localization precision for an indoor mobile robot.     

Trajectory tracking optimization of mobile robot using artificial immune system

In this paper, an optimization method that provides quick response using artificial immune system, is proposed and applied to a mobile robot for trajectory tracking. The study focuses on the immune theory to derive a quick optimization method that puts emphasis on immunity feedback using memory cells by the expansion and suppression of the test group rather than to derive a specific mathematical model of the artificial immune system. Various trajectories were selected in mobile environment to evaluate the performance of the proposed artificial immune system. The global inputs to the mobile robot are reference position and reference velocity, which are time variables. The global output of mobile robot is a current position. The tracking controller makes position error to be converged to zero. In order to reduce position error, compensation velocities on the track of trajectory are necessary. Input variables of fuzzy are position errors in every sampling time. The output values of fuzzy are compensation velocities. Immune algorithm is implemented to adjust the scaling factor of fuzzy automatically. The results of the computer simulation proved the system to be efficient and effective for tracing the trajectory to the final destination by the mobile robot.

     

Elasto-geometrical calibration of a hybrid mobile robot considering gravity deformation and stiffness parameter errors

Hybrid mobile robots, which combine the advantages of serial and parallel robots and have the ability to realize processing in situ, have considerable application potential in the field of processing and manufacturing. In this paper, a hybrid mobile robot used for wind turbine blade polishing is presented. The robot combines an automated guided vehicle, a 2-DoF robotic arm, and a 3-RCU parallel module. To improve the accuracy, investigating the elasto-geometrical calibration of the robot is necessary. Considering that the 3-RCU parallel module has weak stiffness along the gravitational direction, the stiffness model was established to estimate the deformation caused by the gravity of the mobile platform, ball screws, and motors. Subsequently, a rigid-flexible coupling error model considering structural and stiffness parameter errors is established. Based on these, a parameter identification method for the simultaneous identification of structural and stiffness parameter errors is proposed herein. For the 2-DoF robotic arm with parallelogram mechanisms, an intuitive error model considering the posture error caused by the parallelogram mechanism errors is established. The regularized nonlinear least squares method was adopted for parameter identification. Thereafter, a compensation strategy for the hybrid mobile robot that comprehensively considers the pose errors of the 3-RCU parallel module and 2-DoF robotic arm is proposed. Finally, a verification experiment was performed on the prototype, and the results indicated that after elasto-geometrical calibration, the maximum/mean of the position and posture errors of the hybrid mobile robot decreased from 3.738 mm/2.573 mm to 0.109 mm/0.063 mm and 0.236°/0.179° to 0.030°/0.013°, respectively. Owing to the decrease in the robot pose errors, the quality of the polished surface was more uniform. The range and standard deviation of roughness distribution of the polished surface were reduced from 0.595 μm and 0.248 μm to 0.397 μm and 0.127 μm. The methods proposed herein have reference significance for elasto-geometrical calibration of other parallel or hybrid robots.