基于ADAMS和MATLAB的双足机器人运动轨迹规划和控制的联合仿真

相比ADAMS仿真的不稳定性,利用ADAMS和MATLAB联合仿真对双足机器人运动轨迹规划和控制设计的可靠性及高效性进行了研究。首先绘制双足机器人三维参数模型进行逆运动学分析,验证其合理性后导入ADAMS中添加约束,进行动力学仿真。基于此,在MATLAB中使用多项式插值法完成模型的步态规划。最后利用Simulink试验台建立控制系统的框图,由ADAMS输入关节角之后控制台输出关节的驱动力矩,完成双足机器人ADAMS和MATLAB的联合仿真。仿真结果显示,联合仿真相较于ADAMS仿真的波动性所获得的数据更加稳定,此法高效可行,可作为下一步设计双足机器人的控制系统电机选型的重要理论依据。     

基于软件校准的短波发射机功率控制系统设计

短波发射机功率稳定一直是通信领域致力改善的重点问题,短波发射机功率不稳定会直接影响无线电通信质量,造成通信失真、表达不清晰等问题。针对上述问题,基于软件校准设计短波发射机功率控制系统。该系统借鉴MVC设计模式搭建系统数据库层、业务逻辑层、控制层以及界面显示层基础框架；将功率计与短波发射机相连,实时采集工作状态下的短波发射机功率数据,通过信号处理器实施处理后并存储,借鉴传输元件,将数据发送到控制器,通过控制器校准短波发射机功率与预期之间的偏差,以偏差量为输入,利用改进PID运算得出控制量,生成控制命令,通过输入输出信号接口板输出命令,控制驱动装置调节短波发射机运行参数,实现功率控制。结果表明：与 控制系统、自动调谐系统应用相比较,在所设计系统应用控制下,100s内短波发射机的功率变化曲线与预期曲线之间的拟合优度指数更大,更接近1,优于对比系统,说明相比于对比系统。本系统控制表现更好,更能维持短波发射机功率稳定,达到了研究目标。     

Influence of CO2 inleakage on the slight-alkalization of generator internal cooling water

The slight-alkalization of generator internal cooling water (GICW) is widely used to inhibit the corrosion of hollow copper conductor and thereby ensure the safe operation of the generator. CO₂ inleakage is increasingly identified as a potential security risk for GICW system. In this paper, the influence of CO₂ inleakage on the slight-alkalization of GICW was theoretically discussed. Based on the equilibriums of the CO₂-NaOH-H₂O system, CO₂ inleakage saturation was derived to quantify the amount of the dissolved CO₂ in GICW. This parameter can be directly calculated with the measured conductivity and the [Na⁺] of GICW. The influence of CO₂ inleakage on the slight-alkalization conditioning of GICW and the measurement of its water quality parameters were then analyzed. The more severe the inleakage, the narrower the water quality operation ranges of GICW, resulting in the more difficult the slight-alkalization conditioning of GICW. The temperature calibrations of the conductivity and the pH value of GICW show non-linear correlations with the amount of CO₂ inleakage and the NaOH dosage. This study provides insights into the influence of CO₂ inleakage on the slight-alkalization of GICW, which can serve as the theoretical basis for the actual slight-alkalization when CO₂ inleakage occurs.     

Piecewise constant strain kinematic model of externally loaded concentric tube robots

This paper presents a piecewise constant strain kinematic model for concentric tube robots (CTR) in externally loaded conditions. It discretizes the pre-curved tubes comprising the robot into a finite number of pieces and involves external effects as a set of wrench vectors exerted along the robot backbone. Constant strain lets us describe the pieces with helices in which shear deformation and elongation are neglected. The resulting piecewise helix is the simplest curve that can catch the torsion of tubes that play a crucial role in kinematic behavior. This approximation transforms the conventional boundary value problem (BVP) of CTRs models into a set of nonlinear equations that drastically decreases the model resolution time. The present method uses a Lyapunov function and torsional Jacobian to ensure the distal torsion constraint consistently and, as a result, the solution’s convergence. The paper’s primary purpose is to present a fast, numerically stable, and relatively accurate kinematic model not reliant on measurement data. Experimental results on a two-tube prototype and provided for different tip loading conditions reveal maintaining a balance between adequate accuracy and reasonable running time, about 7 ms for five pieces per section, for real-time applications in the presence of external load.     

Investigating measurements of fine particle (PM2.5) emissions from the cooking of meals and mitigating exposure using a cooker hood

There is growing awareness that indoor exposure to particulate matter with diameter ≤ 2.5 μm (PM_2.5) is associated with an increased risk of adverse health effects. Cooking is a key indoor source of PM_2.5 and an activity conducted daily in most homes. Population scale models can predict occupant exposures to PM_2.5, but these predictions are sensitive to the emission rates used. Reported emission rates are highly variable and are typically for the cooking of single ingredients and not full meals. Accordingly, there is a need to assess PM_2.5 emissions from the cooking of complete meals. Mean PM_2.5 emission rates and source strengths were measured for four complete meals. Temporal PM_2.5 concentrations and particle size distributions were recorded using an optical particle counter (OPC), and gravimetric sampling was used to determine calibration factors. Mean emission rates and source strengths varied between 0.54—3.7 mg/min and 15—68 mg, respectively, with 95% confidence. Using a cooker hood (apparent capture efficiency > 90%) and frying in non‐stick pans were found to significantly reduce emissions. OPC calibration factors varied between 1.5 and 5.0 showing that a single value cannot be used for all meals and that gravimetric sampling is necessary when measuring PM_2.5 concentrations in kitchens.     

基于参数预标定的长行程PMLSM无传感器控制

为了解决长行程定子不连续永磁直线同步电机存在的因无法全程安装位置传感器和不同动子和定子之间的电磁参数不固定所造成的控制性能下降的难题,提出一种在每一段定子内先进行参数标定,再进行速度控制的控制系统设计。首先,在动子进入过程中,对电机进行电磁参数标定,根据标定参数对控制器参数进行调整,以达到更好的控制效果。然后,使用无位置传感器控制系统使动子快速达到设定速度值并稳定运行。实验结果表明:动子进入过程参数标定精度分别为0.002Wb和0.000 4H;无位置传感器控制中位置估计精度为0.63mm,速度收敛时间为0.45s,稳态误差为0.02m/s。基本满足永磁直线同步电机用于长行程运输的控制快速性、稳定性等要求。     

卷积神经网络用于关节角度识别与姿势评估

为了快速准确地输出各种工作姿势风险评估结果，提出采用Kinect v2与卷积神经网络识别人体各关节角度，并输出标准姿势风险的评估得分。首先使用亚像素角点提取的棋盘标定算法标定Kinect两个摄像头，其次使用改进后的双边滤波对深度图像去噪，使用卷积神经网络识别人体关节二维位置，结合深度信息获取实际三维坐标并计算人体关节角度，最后输出姿势风险评估得分。通过两种实验分别验证了提出的Kinect角度识别与姿势评估的准确性，表明该方法关节角度识别与姿势风险评估的准确率均较高，是一种低成本、高可靠性的姿势评价方法，具有一定的科学意义和工程应用价值。     

基于差分进化算法的手眼标定方法

针对眼在手机器人视觉系统的手眼标定问题，提出了一种基于差分进化算法的手眼标定方法。首先建立了眼在手机器人视觉系统手眼标定的数学模型，通过李群李代数理论，将手眼标定问题转化为误差函数的优化问题，同时保证了优化问题的最优解落在特殊欧氏群ＳＥ（３）上。通过差分进化算法对优化问题进行求解，避免了求解过程中的局部最优问题。最后在实物平台上进行了手眼标定实验，实验结果验证了算法的可行性和有效性。     

Modelling and predicting the effect of social distancing and travel restrictions on COVID-19 spreading

To date, the only effective means to respond to the spreading of the COVID-19 pandemic are non-pharmaceutical interventions (NPIs), which entail policies to reduce social activity and mobility restrictions. Quantifying their effect is difficult, but it is key to reducing their social and economic consequences. Here, we introduce a meta-population model based on temporal networks, calibrated on the COVID-19 outbreak data in Italy and applied to evaluate the outcomes of these two types of NPIs. Our approach combines the advantages of granular spatial modelling of meta-population models with the ability to realistically describe social contacts via activity-driven networks. We focus on disentangling the impact of these two different types of NPIs: those aiming at reducing individuals’ social activity, for instance through lockdowns, and those that enforce mobility restrictions. We provide a valuable framework to assess the effectiveness of different NPIs, varying with respect to their timing and severity. Results suggest that the effects of mobility restrictions largely depend on the possibility of implementing timely NPIs in the early phases of the outbreak, whereas activity reduction policies should be prioritized afterwards.