基于5G通信技术的巡逻机器人定位误差自动补偿方法

以提升巡逻机器人执行巡逻任务能力为目的,提出基于5G通信技术的巡逻机器人定位误差自动补偿方法。该方法在巡逻机器人工作区域架设5G无线通信网络,并将AD7380型号位置传感器安装在巡逻机器人上,利用位置传感器获取巡逻机器人工作时的位置信息后,利用5G无线通信网络将其传输到用户PC端,得到巡逻机器人位置采样点数据；以该数据为基础,使用拉丁超立方采样方法描述巡逻机器人在其巡逻空间内的位置,得到巡逻机器人空间位置数据；再依据巡逻机器人空间位置数据,计算该机器人预设目标点误差矢量,并构建巡逻机器人定位误差补偿模型,利用该模型补偿巡逻机器人定位误差。实验结果表明：该方法可全面获取巡逻机器人在其巡逻空间内的位置信息,可有效且精准的对其定位误差进行自动补偿,使巡逻机器人定位误差保持在可允许范围内,应用效果较为显著。     

A system decomposition method for region tracking control of a non-holonomic mobile robot with dynamic parameter uncertainties

The tracking control problem of non-holonomic mobile robot systems has been extensively investigated in the past decades, however, most of the existing control strategies were developed specifically for the fixed-point tracking. This technical note focuses on the region tracking control for a non-holonomic mobile robot system with parameter uncertainties in the robot dynamics. With the system decomposition and adaptive control method, some restrictions imposed on the angular and linear velocities of the non-holonomic mobile robot in recent literature are removed, enabling to track dynamic trajectories with any values of the angular and line velocities. The proposed adaptive control scheme can simultaneously solve both the regulation and region tracking problems of a non-holonomic mobile robot with one passive wheel and two actuated wheels. By utilizing the designed control laws, the mobile robot system is able to globally reach inside a moving region specified by potential functions whose path can be a circular curve, a straight line, or sinusoidal curve, by using a single adaptive controller. Since the dynamic region can be specified arbitrarily small, the fixed-point tracking can be regarded as a special case of region tracking studied in this paper. Compared with the traditional fixed-point tracking, region tracking has more flexibility and better robustness. Numerical results are presented to show the effectiveness of the designed strategy.     

A New assembly precision prediction method of aeroengine high-pressure rotor system considering manufacturing error and deformation of parts

The accurate prediction of high-pressure rotor system assembly precision before assembly is the premise of improving aeroengine assembly quality and performance. The existing assembly precision prediction models (APPM) only consider the manufacturing error factors of parts, but rarely involve the deformation of parts under load, so there is a certain gap between the prediction results and the actual situation. This article studies the construction method of APPM considering the manufacturing error and deformation factors of parts. Firstly, the fitting algorithm is used to obtain the fitting deformation surface(FDS) of each mating surface under load, which provides the basis for constructing assembly error model considering manufacturing error and deformation of parts; secondly, according to the relative position relationship between the FDS and the datum plane, the error model of each mating surface of the assembly is effectively constructed by the small displacement torsor theory; thirdly, according to the different errors of each fitting end face, a prediction model of assembly precision for two rough surfaces is constructed by homogeneous coordinate transformation method; finally, a high-pressure compressor rotor system is used as an example to verify the effectiveness of the precision prediction model. The results show that the prediction results based on the proposed model are closer to the actual conditions. This paper provides an effective prediction model for high-pressure rotor system assembly precision, and has important application value for improving the assembly quality and performance of aeroengine.     

称重传感器非线性误差自适应补偿方法

额定量程内称重传感器的非线性误差不同,为此阐述了称重传感器的非线性误差特性,提出了一种非线性误差自适应分段补偿方法:在额定量程的上限区,采用基于径向基函数神经网络(RBFNN)的补偿网络完成传感器非线性误差补偿;在下限区,采用数字滤波器完成非线性误差补偿;在中间区,传感器不补偿。同时利用自适应选择网络,完成了分段补偿的选择。实验表明,采用这种方法补偿后的称重传感器下限区、中间区与上限区的最大相对误差分别由补偿前的0.2、0.4、1.37下降到0.16、0.04、0.07,补偿效果明显。     

Accurate error compensation for a MR-compatible surgical robot based on a novel kinematic calibration method

Kinematic calibration is an effective and economical way to improve the accuracy of surgical robot, and in most cases, it is a necessary procedure before the robot is put into operation. This study investigates a novel kinematic calibration method where the effect of controller error is taken into account when formulating the model based on screw theory, which is applied to the kinematic control of magnetic resonance compatible surgical robot. Based on screw theory, the kinematic error model is established for the relationship between error of controller and the deviation of the measured pose of the end-effector. Therefore, the error of controller can be figured out and parameters of controller can be adjusted accordingly. Control strategy based on the kinematic calibration framework is proposed. According to artificial neural network, the deviation of end-effector in arbitrary configuration can be effectively obtained. Comparative experiments are carried out to show the validity and effectiveness of the proposed framework with the help of commercial visual system and joint encoders.     

A closed-loop error compensation method for robotic flank milling

Errors of diverse sources prevented industrial robots from being adopted into milling applications. This paper proposes a closed-loop error compensation method for robotic flank milling of complex shaped surfaces. First, the finished surface is measured in situ by a laser tracker based measuring system without unclamping the fixture. Then, the sampled points are mapped into the model reference coordinate system and a deterministic bicubic B-spline surface is fitted to extract the systematic components of the machining errors. Finally, the compensation tool path is directly generated for the mirror symmetry points of the only-systematic-error-contained sample points. The robot motion program is converted accordingly for further machining. The experiment shows that the surface accuracy is improved significantly in terms of the profile error via the proposed error compensation process, which well validates the effectiveness of the method.     

基于RBFNN的称重传感器温度误差补偿

称重传感器存在因环境温度不同导致的非线性误差,需要进行补偿.阐述了称重传感器的温度误差机理,提出了一种基于径向基函数神经网络(RBFNN)的称重传感器温度误差补偿方法,并给出了训练算法.采用该方法,利用量程为100kg的称重传感器,在0～60℃范围内进行温度误差补偿实验.实验表明:采用这种方法补偿后,称重传感器温度误差...     

基于非线性误差补偿的蓝藻水华时序综合预测方法

针对现有蓝藻水华预测方法中大多仅采用智能模型或时序模型无法准确描述水华生成过程、预测精度不高等问题,本文根据蓝藻水华生成过程中同时存在的非平稳及非线性变化特点,在水华多元非平稳时序模型预测基础上,采用多种智能非线性模型对其非线性预测误差进行预测及补偿,从而提出将多元非平稳时序模型与智能模型相结合的蓝藻水华综合预测方法。本方法首先对蓝藻水华采用多元非平稳时序模型预测并提取非线性预测误差,通过核主成分分析法对影响预测误差的各因素进行分析,利用适于非线性系统建模的遗传算法(GA)优化最小二乘支持向量机(LSSVM)模型及BP神经网络模型对时序模型的非线性预测误差进行预测并补偿,从而提高水华预测精度。本文针对同一批蓝藻水华数据分别采用多元非平稳时序、BP、LSSVM、GA-BP、GA-LSSVM五种方法进行对比分析,实验结果表明,所提出的误差预测模型得到的蓝藻水华预测结果相比仅采用多元非平稳时序方法更符合实际,同时,GA-BP及GA-LSSVM模型的非线性误差预测结果相比BP及LSSVM精度更高,而在小样本情况下,GA-LSSVM模型的预测结果相比GA-BP模型精度更高,稳定性更好。因此本方法解决了现有的蓝藻水华预测精度不高、单一模型建模容易丢失信息等问题,提高了蓝藻水华建模预测的效果。     

一种用于冲击性负荷的动态无功功率补偿方法

针对冲击性负荷造成的供电系统功率因数低、电压跌落和闪变等电能质量问题,提出了一种快速晶闸管投切电容器动态无功补偿方法。该方法采用基于改进瞬时无功理论的无功电流检测算法和新型不完全微分PID控制算法对冲击性负荷进行快速无功补偿。实验结果表明,该方法不仅可以提高系统功率因数,而且可以稳定变压器输出端电压,增强系统的控制精度和响应速度。     

传感器实时动态补偿方法与误差分析

为了改善传感器动态特性,用零极点相消的补偿原理拓宽传感器系统的工作频带.研究了极点建模误差对传感器补偿效果的影响.分析一阶系统、二阶系统的极点建模误差与工作频带拓宽之间的关系,给出相应的关系图.得出结论:一阶系统,补偿效果只与建模误差有关,补偿效果较明显;二阶系统,补偿效果由误差与阻尼比决定,阻尼比越大,要求其极点实部精度越高,而其虚部精度要求越小,且阻尼比小于0.1时,其实部误差可以忽略不计,仿真结果与理论一致.定量分析补偿后噪声的放大程度与工作频带拓宽之间的关系,为实时补偿提供依据.并在数字信号处理器(DSP)中实现实时补偿系统,实时观察输出结果,验证了该系统的稳定性和可行性.     

考虑模型误差的浸出过程优化方法

由于浸出过程较为复杂,其过程模型难以准确地反映实际过程,导致基于该模型的过程优化结果不是实际最优值.基于此,提出一种考虑模型误差的浸出过程优化方法,利用高斯混合模型对浸出过程混合模型的误差均值和方差进行描述,并将其引入优化目标中.构建考虑模型误差的浸出过程优化模型,并以二阶振荡粒子群优化算法完成对优化模型的求解.最后通过仿真实验表明了所提出方法的有效性.     

采用柯西分解降噪的目标跟踪方法

提升噪声干扰情况下的目标跟踪精度,提出了一种柯西分解改进的无迹[H∞]滤波方法。采用UT变换代替传统的泰勒级数线性近似截断处理,降低近似截断误差,提升处理非线性系统的能力;对目标状态的协方差矩阵均方根进行柯西分解,采用对角元素求解,降低了观测噪声的扰动误差对滤波估计精度的影响。仿真结果表明,所提方法明显提升了不同噪声统计特性情况下的跟踪精度和稳定性。     

一种基于路况的渐消记忆陀螺误差补偿方法

针对GPS/DR组合导航系统在汽车直行和转弯时速率陀螺误差特性不同的情况,提出了一种基于路况的渐消记忆陀螺误差补偿方法。这种方法在GPS信号正常时根据路况不同实时计算陀螺刻度因子误差;当GPS丢星时,用GPS正常时计算的刻度因子误差补偿速率陀螺。在实际工程应用中,用此方法补偿低成本GPS/DR组合导航系统的陀螺误差后再进行EKF容错滤波,在GPS丢星后一段时间内,仍能保持较好的导航精度。     

考虑频域分解后数据特征的空调负荷预测模型

针对空调负荷预测实际应用中容易存在数据散杂且可用信息匮乏的问题,从负荷序列的非线性、非平稳性和随机性出发,提出了一种基于变分模态分解(VMD)的负荷预测方法.对不同数据特征序列考虑不同算法的数据观测与训练原理差异,充分发挥各个模型优势.首先采用随机森林(RF)进行特征选择,利用VMD将负荷序列按趋势分量、平稳分量和噪声分量进行分类重构,并分别对非线性序列建立最小二乘支持向量机(LSSVM)预测模型,时序平稳序列建立极端梯度提升(XGBoost)预测模型,采用正态分布拟合随机误差,得到各子序列预测结果并进行叠加输出最终负荷预测结果.实验结果表明,所提方法能准确反映负荷的特性并具有更好的预测精度,能有效预测空调负荷,为空调节能优化控制策略提供依据.     

三轴地磁传感器误差建模及在线补偿方法

针对三轴地磁传感器的传统误差补偿方法操作繁琐、依赖外界仪器,无法应用于车辆地磁导航系统的缺点,提出了一种基于无迹卡尔曼滤波的三轴地磁传感器误差在线补偿方法。分析地磁传感器误差产生机理,得到了适用于车辆导航系统的地磁传感器误差模型。设计无迹卡尔曼滤波器估计模型中未知参数,从而实现误差的在线补偿。实验结果表明:所提方法有效可行,补偿后的精度在0. 5°以内,补偿效果较好。     

姿态航向测量与误差补偿方法研究

设计了一种基于MEMS陀螺仪、加速度计、磁传感器的小型姿态航向参考系统;以四元数和角速率偏差为状态矢量,磁场强度和加速度计信息为量测矢量,构建基于Kalman的四元数姿态航向解算方法;通过调整测量噪声方差矩阵,解决动态过程中由于运动加速度造成的姿态角误差;采用陀螺仪误差建模和磁航向罗差补偿技术,进一步提高了系统测量精度。根据飞行数据分析,姿态航向参考系统具有较高测量精度和较好的稳定性、动态性,姿态角均方根误差小于1.5°,航向角均方根误差小于3°。     

基于节能考虑的全向移动机器人鲁棒补偿轨迹跟踪控制

针对全向移动机器人在跟踪目标的过程中存在跟踪误差以及产生能量损耗的问题,首先构建一种新型机器人能耗模型,该模型能够有效预测机器人运行过程中各种能量消耗;其次,基于该能耗模型设置兼顾轨迹跟踪误差和能耗最小的性能指标函数,在其约束下,提出一种基于干扰鲁棒补偿的反馈节能控制器;然后,引入不确定性及干扰估计观测法,构建鲁棒补偿项,在满足能耗最优的前提下实现对外界干扰的有效抑制;最后,基于Lyapunov稳定性理论证明所提出的节能干扰鲁棒补偿控制系统是渐近稳定的.通过将所提出的控制器与比例微分控制器、$H_infty$控制以及节能补偿控制进行比较,仿真结果表明,所提出的控制方法其控制精度更高、能量损耗更低、具有更强的鲁棒性.     

载人机器人平顺轨迹规划及自抗扰跟踪控制方法

针对公共环境载人机器人的安全性和乘载舒适性需求,提出一种机器人平顺轨迹规划及改进自抗扰跟踪控制方法,重点解决避险和平稳运动两种工况下机器人轨迹扰动问题;首先,设计了基于障碍物密度及其危险性的在线速度空间优化方法,获得符合安全和舒适度要求的轨迹;其次,采用扩张状态观测器观测机器人轨迹跟踪过程中内外扰动并实施补偿;再次,引入非线性反馈控制律自动调整反馈增益,减少超调并提高自抗扰控制器稳定性;对不同场景数值模型仿真表明,提出的轨迹规划方法可以满足机器人作业的安全性、舒适性需求;改进自抗扰跟踪控制器效果明显优于传统ADRC和自适应PID,具有较强的抗干扰能力和较快速的跟踪性能.     

具有NN 分级误差补偿器的轧制力预报模型

为提高冷轧带钢质量，必须建立精确的轧制力预报模型。介绍了带有神经网络参数辨识器的轧制力预报模型，并采用神经网络构造误差补偿器，通过按时间跨度分级的数据处理过程，形成长期和短期训练数据集，结合相应的网络训练机制，实现模型预报误差的分级补偿，仿真结果表明，该预报模型能有效地提高预报精度。     

Object tracking for mobile robot based on intelligent method

This paper proposed the real-time tracking algorithm in the active camera system, which is based on the intelligent method. To separate the object from background, the similarity of the color is analyzed through the fuzzy inference engine. And after segmentation, the local difference method between the continuous images is used to track the moving object. The experiment is performed using the developed embedded camera system with an ARM processor. This work was presented in part at the 13th International Symposium on Artificial Life and Robotics, Oita, Japan, January 31–February 2, 2008