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。     

人体步行规律与仿人机器人步态规划

从仿生学角度分析了人体的步行运动规律,提出了一种基于人体运动规律的仿人机器人步态参数设定方法.首先对人体步行运动数据进行捕捉并分析,得出人体各步态参数间的函数关系,以人体步行相似性作为评价指标,提出仿人机器人步态参数的设定方法.其次,通过分析人体在步行过程中的补偿支撑脚偏航力矩的基本原理,提出了基于双臂及腰关节协调运动的仿人机器人偏航力矩补偿算法,以提高仿人机器人行走的稳定性.最后通过仿真及实验验证了所提出的步态规划方法的正确性及有效性.     

基于随机经济模型预测控制的电热综合能源系统运行优化

北方供暖地区存在大量热电联产机组,其"以热定电"的运行特性限制了机组调峰能力.同时,随着我国能源替代的提出以及风电的快速发展,建立包含风电的电热综合能源系统是降低电热耦合以及系统运行成本的重要手段.因此,建立电热综合能源系统模型,在满足用户电热负荷需求的基础上,综合考虑风电不确定性以及系统动态过程中的经济性,提出一种基于情景树结构的随机经济模型预测控制策略.为了降低风电功率随机性干扰和风电功率变化难以描述的问题,提出采用一种改进的非线性变换方法建立风电功率非高斯分布模型,在此基础上建立基于情景树结构的风电功率变化显式表达结构,在保证鲁棒性的同时降低了优化过程中风电随机性的干扰.最后,通过累计节约成本的复合单步增长率指标验证所提策略的有效性.     

Asynchronous decentralized event-triggered control

In this paper we propose an approach to the implementation of controllers with decentralized strategies triggering controller updates. We consider set-ups with a central node in charge of the computation of the control commands, and a set of not co-located sensors providing measurements to the controller node. The solution we propose does not require measurements from the sensors to be synchronized in time. The sensors in our proposal provide measurements in an aperiodic way triggered by local conditions. Furthermore, in the proposed implementation (most of) the communication between nodes requires only the exchange of one bit of information (per controller update), which could aid in reducing transmission delays and as a secondary effect result in fewer transmissions being triggered.     

Hierarchical abnormal event detection by real time and semi-real time multi-tasking video surveillance system

In this paper, we describe how to detect abnormal human activities taking place in an outdoor surveillance environment. Human tracks are provided in real time by the baseline video surveillance system. Given trajectory information, the event analysis module will attempt to determine whether or not a suspicious activity is currently being observed. However, due to real-time processing constrains, there might be false alarms generated by video image noise or non-human objects. It requires further intensive examination to filter out false event detections which can be processed in an off-line fashion. We propose a hierarchical abnormal event detection system that takes care of real time and semi-real time as multi-tasking. In low level task, a trajectory-based method processes trajectory data and detects abnormal events in real time. In high level task, an intensive video analysis algorithm checks whether the detected abnormal event is triggered by actual humans or not.     

基于双向搜索的改进蚁群路径规划算法

针对移动机器人在复杂地图环境中移动耗时长、易陷入局部最优等问题,设计了一种基于双向搜索的改进蚁群路径规划算法。基于K-means算法对地图预处理,量化地图的局部复杂度程度,并将局部环境信息融合到状态转移概率函数,使机器人优先选择在复杂程度小的区域进行寻优,减少路径拐点。设定双向搜索规则,改进启发函数,提高算法的局部方向搜索精度和全局搜索效率。针对蚁群算法中蚂蚁遇到U障碍物陷入死锁的问题,提出死锁判断系数,增加了有效蚂蚁的数量,进一步提高了算法性能。仿真结果表明所设计的算法在复杂地图环境中相较于传统蚁群算法移动机器人的路径搜索效率更高。     

基于虚拟力引导的人机协同目标抓取方法

针对力反馈遥操作中传统人工势场法无法适应于机械臂整体的避障以及在作业过程中操作者难以控制机械臂到达所需位姿的问题,提出了一种基于虚拟力引导的人机协同目标抓取方法。力反馈设备向操作者提供力觉交互。通过结合人工势场法和虚拟夹具,构建管道形虚拟力场,生成实时虚拟力引导,实现协助操作者完成从端机器人的整体避障任务并在完成避障后引导机器人返回预定义路径并趋近目标点。当进行抓取任务时,构建锥形虚拟力场,实现协助操作者操作机械臂到达目标位置和姿态。此外,提出了一种机器人运动限制方法以降低操作者的操作失误对抓取任务的影响。实验证明,该方法能有效提高目标抓取操作的成功率和操作效率。     

基于改进广义预测控制的PMSM速度控制

在机器人控制领域中,对永磁同步电机(PMSM)响应的快速性及稳定性要求较高,同时还需要其控制方法有较强的适应性。但传统的PID控制参数只适用于特定场合,应用受到限制。本文结合广义预测控制(GPC)理论与Luenberger观测器,形成一种新的控制方法来对PMSM进行速度闭环控制。该算法与传统GPC算法相比计算量更少,并通过负载转矩观测器对负载扰动进行测量反馈,提高了系统控制性能。仿真及试验结果表明,该算法与PID控制比超调量小,响应速度快,适用于要求较高的工程应用。     

Comparing lazy and eager learning models for water level forecasting in river-reservoir basins of inundation regions

This study developed a methodology for formulating water level models to forecast river stages during typhoons, comparing various models by using lazy and eager learning approaches. Two lazy learning models were introduced: the locally weighted regression (LWR) and the k-nearest neighbor (kNN) models. Their efficacy was compared with that of three eager learning models, namely, the artificial neural network (ANN), support vector regression (SVR), and linear regression (REG). These models were employed to analyze the Tanshui River Basin in Taiwan. The data collected comprised 50 historical typhoon events and relevant hourly hydrological data from the river basin during 1996–2007. The forecasting horizon ranged from 1 h to 4 h. Various statistical measures were calculated, including the correlation coefficient, mean absolute error, and root mean square error. Moreover, significance, computation efficiency, and Akaike information criterion were evaluated. The results indicated that (a) among the eager learning models, ANN and SVR yielded more favorable results than REG (based on statistical analyses and significance tests). Although ANN, SVR, and REG were categorized as eager learning models, their predictive abilities varied according to various global learning optimizers. (b) Regarding the lazy learning models, LWR performed more favorably than kNN. Although LWR and kNN were categorized as lazy learning models, their predictive abilities were based on diverse local learning optimizers. (c) A comparison of eager and lazy learning models indicated that neither were effective or yielded favorable results, because the distinct approximators of models that can be categorized as either eager or lazy learning models caused the performance to be dependent on individual models.     

面向三维结构视觉检测的无人机覆盖路径规划

为了高效地规划无人机在三维覆盖检测任务的飞行路径,建立了满足覆盖率要求的路径规划模型,可分为两步：第1步确定无人机巡检的视点和视线,第2步计算视点的无碰撞访问序列。首先,从巡检目标的三维点云出发,提出基于K均值聚类生成候选视点的方法,构建候选视点互连的非完全图模型;其次,提出面向排序的混合蚁群算法(sorting-oriented hybrid ant colony algorithm, S-HACO)求取无人机巡检路径,优化目标考虑了路径长度、视点数目、急转弯次数等。仿真实验结果表明,该方法得到的视点数目较偏移法和随机采样法分别减少了96.25%和42.10%,并且S-HACO算法较传统算法相比性能更优,目标函数降低了19.14%,算法的运行时间减少了25.27%,验证了模型的有效性和算法的可行性。