跨行政区游憩空间柔性一体化研究 ——以高莱茵河沿岸地区为例

随着城市化与区域一体化的迅速发展,不同行政区之间的联动关系越来越紧密,这种联动关系在城市各功能空间的设计上有所体现,更渗透到了与市民息息相关的游憩空间建构之中。受制于行政管理等方面的原因,在跨行政区的游憩空间一体化实施过程中,往往会采取一系列柔性措施。以欧洲高莱茵河沿岸地区为研究对象,分析高莱茵河沿岸地区游憩空间的发展与现状,总结其游憩空间体系的构建特点,为中国跨城市河流沿岸的游憩空间柔性一体化建构提供策略与建议。     

基于光刀图像的机器人示教纠偏方法

为了解决机器人辅助在线检测系统示教过程中效率低、人为干预多的问题,针对孔类特征,提出一种通过处理光刀图像获取机器人位姿纠正参数的方法。在该方法中,纠偏过程按照将图像光刀线调整为水平方向、将被测特征调整至图像中央、根据不同特征调整最优扫描方向3个步骤进行。根据光刀图像可以求出测量坐标系与被测特征的位姿关系,并得到该位姿与理想测量位姿的偏差。经过坐标变换即可得到在机器人基坐标系下的调整参数。另外,可利用电脑控制机器人运动,从而实现了纠偏过程的自动化。实验结果表明,该方法可将初始的示教位姿修正为理想位姿,且比传统方式效率更高。     

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VMD和BSA-KELM的高陡边坡位移预测模型研究

边坡位移的时间序列曲线存在复杂的非线性特性,传统的预测模型精度不足以满足预测要求。为此提出了基于变分模态分解的鸟群优化-核极限学习机的预测模型,并用于河北省某水泥厂的边坡位移预测。该方法首先采用VMD把边坡位移序列分解为一系列的有限带宽的子序列,再对各子序列分别采用相空间重构并用核极限学习机预测,采用鸟群算法优化相空间重构的嵌入维度和KELM中惩罚系数和核参数三个数值,以取得最优预测模型。最后将各个子序列预测值叠加,得到边坡位移的最终预测值。结果表明：和KELM、BSA-KELM、EEMD-BSA-KELM模型相比,基于VMD的BSA-KELM预测精度更高,为边坡位移的预测提供一种有效的方法。     

Shared human–robot path following control of an unmanned ground vehicle

This paper considers the shared path following control of an unmanned ground vehicle by a single person. A passive measure of human intent is used to blend the human and machine inputs in a mixed initiative approach. The blending law is combined with saturated super-twisting sliding mode speed and heading controllers, so that exogenous disturbances can be counteracted via equivalent control. It is proven that when the proposed blending law is used, the combined control signals from both the human and automatic controller respect the actuator magnitude constraints of the machine. To demonstrate the approach, shared control experiments are performed using an unmanned ground vehicle, which follows a lawn mower pattern shaped path.     

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.     

Efficient Flexible M-Tree Bulk Loading Using FastMap and Space-Filling Curves

Many database applications currently deal with objects in a metric space. Examples of such objects include unstructured multimedia objects and points of interest (POIs) in a road network. The M-tree is a dynamic index structure that facilitates an efficient search for objects in a metric space. Studies have been conducted on the bulk loading of large datasets in an M-tree. However, because previous algorithms involve excessive distance computations and disk accesses, they perform poorly in terms of their index construction and search capability. This study proposes two efficient M-tree bulk loading algorithms. Our algorithms minimize the number of distance computations and disk accesses using FastMap and a space-filling curve, thereby significantly improving the index construction and search performance. Our second algorithm is an extension of the first, and it incorporates a partitioning clustering technique and flexible node architecture to further improve the search performance. Through the use of various synthetic and real-world datasets, the experimental results demonstrated that our algorithms improved the index construction performance by up to three orders of magnitude and the search performance by up to 20.3 times over the previous algorithm.     

Reduced‐order method for mathematical modeling of multiphase dc‐dc converter

A multiphase dc‐dc converter is effective for miniaturization and achieving high‐power density in a switching power supply. However, its mathematical modeling becomes complex as the phase number of the circuit increases. This study proposes a new modeling method to derive a reduced‐order method in a simple manner. The frequency characteristics of the reduced‐order model are fit to those of the original mathematical model of the multiphase dc‐dc converter. Therefore, the efficacy of the proposed method is validated.     

Human responses to high levels of carbon dioxide and air temperature

In this study, 30 subjects were exposed to different combinations of air temperature (T_a: 24, 27, and 30°C) and CO₂ level (8000, 10 000, and 12 000 ppm) in a high-humidity (RH: 85%) underground climate chamber. Subjective assessments, physiological responses, and cognitive performance were investigated. The results showed that as compared with exposure to T_a = 24°C, exposure to 30°C at all CO₂ levels caused subjects to feel uncomfortably warm and experience stronger odor intensity, while increased mental effort and greater intensity of acute health symptoms were reported. However, no significant effects of T_a on task performance or physiological responses were found. This indicated that subjects had to exert more effort to maintain their performance in an uncomfortably warm environment. Increasing CO₂ from 8000 to 12 000 ppm at all T_a caused subjects to report higher rates of headache, fatigue, agitation, and feeling depressed, although the results were statistically significant only at 24 and 27°C. The text typing performance and systolic blood pressure (SBP) decreased significantly at this exposure, whereas diastolic blood pressure (DBP) and thermal discomfort increased significantly. These effects suggest higher arousal/stress. No significant interaction effect of T_a and CO₂ concentration on human responses was identified.     

面向草莓抓取的气动四叶片软体抓手研制

农林业中果蔬的自动化采摘需求日趋强烈,末端抓手是实现无损采摘的关键。传统的末端抓手以刚性结构居多,现有的各种柔性抓手也存在抓取力不足、包覆性不佳等缺点。本文以草莓的无损采摘为研究对象,提出将草莓外部轮廓曲线作为设计曲线,设计了一种新型气动四叶片软体抓手。首先,对软体抓手的结构做仿真优化,提出一种安全地附着在目标物表面的设想。然后,在进行草莓表面的最小破坏应力试验的基础上,测试了软体抓手的末端力,验证了其实现无损抓取的可行性。再次,利用动态捕捉技术,研究了软体抓手叶面的弯曲变形规律。最后,选择使用弧线型气体通道的软体抓手进行了草莓抓取测试,结果证明了气动四叶片软体抓手可以实现草莓的无损抓取,抓取成功率达90%,破损率为2%,表明所研制的四叶片软体抓手用于草莓抓取时具有良好的稳定性和实用性,可用于草莓采摘的末端执行器。本研究也可为其他易损果蔬的采摘技术提供理论基础和技术支撑。