霍夫空间中多足球机器人协作目标定位算法

针对嵌入式仿人足球机器人提出一种霍夫空间中的多机器人协作目标定位算法。机器人利用实验场地中的标志物采用基于三角几何定位方法进行自定位,把机器人多连杆模型进行简化,通过坐标系位姿变换把图像坐标系转换到世界坐标系中,实现机器人目标定位;在多机器人之间建立ZigBee无线传感器网络进行通信,把多个机器人定位的坐标点进行霍夫变换,在霍夫空间中进行最小二乘法线性拟合,获取最优参数,然后融合改进后的粒子滤波实现对目标小球的跟踪;最后在21自由度的仿人足球机器人上进行仿真和实验。数据结果表明,这种多机器人协作的定位算法的精度提高了约48%,在满足实时性的前提下,对目标的跟踪效果也得到了改善。     

机器人三维路径规划问题的一种改进蚁群算法

本文研究移动机器人三维空间路径规划问题,针对三维空间的复杂地形特点,提出了一种基于改进蚁群算法的路径规划算法。文中首先描述了一种简单有效的环境建模方法,然后给出了算法在信息素呈现、路径点选取、信息素更新以及启发式函数设计等方面的改进方法。仿真结果证明了算法的可行性和可靠性。     

基于大学生在线临场感水平调查的计算机公共课网络学习空间构建策略研究

解读在线临场感内涵,并从教学临场感、认知临场感、社会临场感三个维度对大学生在线临场感水平实施问卷调查。从描述性统计和群体差异性统计角度对调查结果加以分析。以计算机公共课《数据库技术及应用》为例,综合调查结果和学科特点对网络学习空间构建提供策略,从而为促进网络学习空间创新发展提供经验积累。     

高空作业平台臂架伸缩运动的横向振动特性

为了探究高空作业平台做伸缩运动时变幅平面内的横向振动动力学特性,针对臂架的实际搭接和支承情况,将臂架等效为根部铰接、中间弹性支承且带有集中参数的变截面变长度梁. 基于牛顿第二定律建立各臂段的运动微分方程,采用模态叠加法、结合边界条件求解臂架一系列时刻的瞬态振型函数,曲线拟合时变参数以近似表示梁的振型,并依据伽辽金截断方法,得到广义坐标下的状态空间方程. 在Matlab/Simulink环境下进行动态仿真,得到伸缩过程中臂架头部的变幅平面横向振动动态响应. 结果表明,就横向振动振幅而言,搭接简化会带来15.63%计算结果误差;支承简化会加大臂架整体刚度、减小臂架振动响应,不可取。     

高温混合障碍空间中的移动机器人路径规划

为了解决高温场景中移动机器人全局路径规划所面临的安全与效率问题,提出高温热源虚拟障碍的定义,建立混合障碍空间模型,将高温场景中的路径规划问题转化为高温混合障碍空间中考虑路径温度代价和长度代价的多目标优化问题. 改进NSGA-Ⅱ算法,通过选取优秀非可行解扩展种群,提高了种群多样性和进化效率,提出新的交叉和变异概率计算方法. 根据种群进化进程和个体代价函数值调整概率,实现了种群前期搜索能力和后期收敛性的平衡. 仿真所得的最优路径结果表明,该改进算法的路径长度代价虽然比原算法和其他改进算法略有增加,但温度代价大幅降低,更有效地避免了陷入局部最优.     

弹性半无限空间中矩形孔收缩的复变函数解答

随着城市建设的发展,矩形隧道的应用越来越多,但针对矩形隧道的理论研究却鲜有见闻。针对矩形隧道,建立了半无限空间矩形隧道的弹性理论计算模型,采用最小二乘迭代方法确定共形映射函数的各项系数,并将计算区域映射为复平面上的一个同心圆环;运用Muskhelishvili复变函数方法,将计算区域内的应力函数展开成为Laurant级数的形式,给定了地表零应力边界和矩形孔口径向位移边界,求得了半无限空间矩形隧道在给定位移条件下的应力场和位移场。分析了不同高宽比、不同泊松比、不同埋深对位移场和应力场的影响,总结了矩形隧道位移场和应力场的一般规律。结果表明：高宽比偏小、泊松比偏大、埋深偏小都会使得沉降槽不再是类高斯曲线的形状,这些参数的变化也会在不同程度上影响应力场和位移场的大小和分布。     

空间高能质子对飞行器材料的损伤分析

针对高能粒子在空间环境中的辐射,本文简要介绍了空间高能质子在空间环境中的分布情况及在物质中的传输问题,评述了国外有关高能质子的非电离能损失及线性能量转移研究,并在此基础上重点分析和研究了高能质子对航空、航天材料的辐射损伤.从空间高能质子对材料的辐射损伤的角度,分析讨论了材料厚度与屏蔽效果的关系并提出了一些相关性的结论和展望;能量为10MeV～80MeV的质子最可能发生辐射损伤效应,质子拟合公式适合于入射质子能量较小的情况.     

Analysis of the physical properties of an in-vessel composting matrix

Efficient compost production requires a thorough understanding of the process dynamics in terms of the feedstock materials used and the interactions of the physical properties involved. The main properties affecting the composting process are temperature, moisture content, bulk density, porosity and oxygen availability. In this study, the correlations between a selected set of physical properties of a batch-composting matrix were determined. The key physical changes in the composting materials for a blend of woodchips, chicken manure and mixed green vegetables have been monitored during a 36-day composting period in a 200-L rotary drum. The daily measurements conducted on the solid samples included temperature, pH, volatile solids, bulk density, moisture content, free airspace and substrates particle density while the carbon dioxide release was monitored weekly using jar respiration tests. The results from the compost process monitoring were a maximum temperature rise to 66.3 °C over the first 3 days, a marked decrease in free airspace from 76.3% to 40.0% at the end of the process, a variation in average composting material particle density from 1097 kg/m³ to 2325 kg/m³, and an increase in wet bulk density from 255 kg/m³ to 628 kg/m³. Correlations developed among free airspace, wet bulk density, dry bulk density and wet moisture content were in agreement with previously determined equations from literature. Free airspace varied linearly with both dry and wet bulk densities (R² value of 0.89 and 0.95, respectively) while the free airspace-wet moisture content profile followed a fourth degree polynomial trend with a correlation coefficient of 0.63.     

Computational Problem Solving in Spatial Substrates -- A Cognitive Systems Engineering Approach

The ability to perform spatial tasks is crucial for everyday life and of great importance to cognitive agents such as humans, animals, and autonomous robots. A common artificial intelligence approach to accomplish spatial tasks is to represent spatial configurations and tasks in form of detailed knowledge about various aspects of space and time. Suitable algorithms then use the representations to compute solutions to spatial problems. In comparison, natural embodied and situated agents often solve spatial tasks without detailed knowledge about geometric, topological, or mechanical laws; they directly relate actions to effects that are due to spatio-temporal affordances in their bodies and environments. Accordingly, we propose a paradigm that makes the spatio-temporal substrate an integral part of the engine that drives spatial problem solving. We argue that spatial and temporal structures in body and environment can substantially support (and even replace) reasoning effort in computational processes: physical manipulation and perception in spatial environments substitute formal computation. While the approach is well known – for example, we employ diagrams as spatial substrate for geometric problem solving and maps for wayfinding – the underlying principle has not been systematically investigated or formally analyzed as a paradigm of cognitive processing. Topology, distance, and orientation constraints are all integrated and interdependent in truly 2- or 3-dimensional space. Exploiting this fact may not only help overcome the need for acquiring detailed knowledge about the interrelationships between different aspects of space; it also can point to a way of avoiding exploding computational complexity that occurs when we deal with these aspects of space in complex real-world scenarios. Our approach employs affordance-based object-level problem solving to complement knowledge-level formal approaches. We will assess strengths and weaknesses of the new cognitive systems paradigm.