关键词： Delta并联机器人   动力学模型   雅克比矩阵   运动性能分析   轨迹规划  

摘要： 随着当前动力成本的提高，工业自动化水平的不断发展，工业机器人正得到越来越广泛的应用，其中最典型的当属汽车行业的自动化装配。并联机构刚度大、承载能力强、精度高且自重负荷比小等特点，使得其成为轻工业自动化装配的主流机械手。 本文以Delta并联机器人为研究对象，建立完整的动力学模型。首先根据其结构特点将机构进行了简化，利用空间矢量关系建立机器人运动的约束方程，得到Delta机器人的位置反解，同时给出了位置正解的几何解法。在此基础上分析了机构的速度、加速度，推导出了机构雅克比矩阵，并且通过对比Matlab仿真结果和实际机器人末端速度验证了模型的正确性。同时对Delta并联机构进行了运动性能的分析，在给定的工作空间内，将机构可操作度和雅克比矩阵条件数作为衡量机构运动性能的指标，利用Matlab编程仿真出了给定机器人机构可操作度和雅克比矩阵条件数在X、Y截面的分布图，仿真结果表明机构的运动性能良好。在此正确模型的基础上，利用仿真和分析结合的方法获取Delta机器人常见轨迹下的最大速度。 本文提出矢量分析法解决该机器人最大速度的理论方法。最后通过分析从动杆的质量惯性矩阵，将模型合理简化。利用虚功原理对简化模型的建模，得到主动臂输出力矩的显示方程。 最后以作业周期最短为设计目标，同时兼顾运动的平稳性，对Delta并联机械手进行初步轨迹规划，写出规划方程。

关键词： 柔性   并联机器人   动力学分析   假设模态法   变长度有限单元  

摘要： 本文以柔索牵引并联机器人为研究对象,柔索牵引并联机器人具有机械效率高、承载能力强、作业精度高、自重负荷小、结构紧凑、动力性能好和易于控制等优点,并广泛的应用于一些对作业精度要求高、作业速度快以及作业效率高的场合。对柔索牵引并联机器人进行研究分析时,需要建立其动力学模型。现有的建模方法或者忽略了柔索的弹性变形,或者简单的将其处理成索杆单元。这样虽然使动力学模型得到简化,但是却不能准确的描述柔索牵引并联机器人系统的运动。因此,以上建模方法存在一定的局限性。为了使所建立的动力学模型能够更加准确的描述柔索牵引并联机器人系统的运动,本文对柔索牵引并联机器人系统的建模方法进行了研究。在本文中,我们在考虑柔索自身重力和弹性变形的情况下,着重研究了两种不同的柔索牵引并联机器人系统的建模方法,一种采用哈密顿原理,从能量的角度出发,运用变分原理和哈密顿原理,建立系统的动力学模型,并采用假设模态法对动力学模型进行求解;另一种是变长度有限单元法,该方法采用绝对节点坐标方法建立系统的动力学模型。在本文中,对这两种不同的建模方法,分别在MATLAB中对其进行了数值仿真,并将数值仿真的结果做了对比分析。结果表明,假设模态法和变长度有限单元法在柔索牵引并联机器人系统建模方面各有特点,它们都可以很好的描述柔索牵引并联机器人系统的运动,但相对来说,采用变长度有限单元法得到的仿真效果更好。

关键词： DELTA机器人   简化动力学   质量矩阵   跟随误差  

摘要： 工业机器人是工业装备制造的基础。机器人在工业领域的应用，不仅提高了生产效率，而且提升了产品的质量。DELTA机器人是少自由度的并联机器人，由于其可以将驱动器放置在底座上，而且具有多条运动链驱动，这种结构可以获得大的刚度、高的运行速度和高的精度。DELTA机器人经常用于取放操作，被广泛应用在食品包装、电子产品等领域。同时由于闭链约束的存在，DELTA机器人的运动学、动力学相对复杂，在工业应用中很难将完备的动力学应用于机器人的运动控制中。针对机器人运动规划中较少考虑机器人的动力学特性、动力学模型编程复杂和复杂的动力学模型难用于工业机器人实时控制的现状，将DELTA机器人的动力学模型进行适当简化，并应用到工业机器人的运动规划和运动控制中很有必要。 首先，简化了DELTA机器人的几何模型，研究了可能存在的八组位置逆解，通过分类讨论的方法确定了选择逆解的方案。同时，完成了正运动学位置、速度和加速度的求解，建立机器人的完整运动学模型，并通过仿真的方式对求解的运动学模型进行验证。 其次，通过虚功原理建立了机器人完备的动力学模型，在对机器人质量矩阵分析的基础上，对机器人的动力学模型进行简化，通过对比的方式验证简化方法的合理性，进一步分析了机器人的质量矩阵在空间的分布。 然后，在使用向量法求取机器人雅克比矩阵的基础上，以雅克比矩阵为依据对机器人可能的奇异位形及其规避方法进行分析。依据简化的动力学模型，在机器人运动规划过程中将驱动器的转速限制和力矩限制考虑在内，求取机器人末端允许的最大速度和加速度。 最后，搭建实验和仿真平台研究控制算法对机器人末端跟踪精度的影响。为了实现机器人动力学模型中惯性项力矩的在线计算，忽略重力项，速度项系数采用常数，离线对机器人的质量矩阵进行多项式拟合，通过PLC程序在线修改控制器参数的方式将驱动力的惯性项力矩加入到控制算法中作为前馈的一部分，分别对得到的关节角度跟随误差和末端位置跟随误差进行比较，以验证带有基于简化动力学模型前馈的控制算法和单一的PID控制算法的不同性能。

关键词： 创新大赛   机器人   长沙市  

摘要： 2014年5月25日,第31届长沙市青少年科技创新大赛暨第七届长沙市青少年机器人竞赛展示活动在湖南省长沙市长郡芙蓉中学隆重举行。在这次活动中,共展出超过200件青少年科技创新大赛获奖作品;展示了青少年开展科技活动、介绍科学知识的200多块展板;组织了机器人项目和模型项目的表演。这次活动得到了中国工程院院士及各级领导的高度重视,出席大会的专家和领导有:中南大学

摘要： On one hand, academic and industrial researchers have been developing and deploying robots that are used as educational tutors, mediators, and motivational tools. On the other hand, an increasing amount of interest has been placed on non- expert users being able to program robots intuitively, which has led to promising research efforts in the fields of machine learning and human-robot interaction. This dissertation focuses on bridging the gap between the two subfields of robotics to provide personalized experience for the users during educational, entertainment, and therapeutic sessions with social robots. In order to make the interaction continuously engaging, the workspace shared between the user and the robot should provide per- sonalized contexts for interaction while the robot learns to participate in new tasks that arise. This dissertation aims to solve the task-learning problem using an instance-based framework that stores human demonstrations as task instances. These instances are retrieved when confronted with a similar task in which the system generates predic- tions of task behaviors based on prior solutions. The main issues associated with the instance-based approach, i. e., knowledge encoding and acquisition, are addressed in this dissertation research using interactive methods of machine learning. This ap- proach, further referred to as interactive instance-based learning (IIBL), utilizes the keywords people use to convey task knowledge to others to formulate task instances. The key features suggested by the human teacher are extracted during the demon- strations of the task. Regression approaches have been developed in this dissertation to model similarities between cases for instance retrieval including multivariate linear regression and sensitivity analysis using neural networks. The learning performance of the IIBL methods were then evaluated while participants engaged in various block stacking and inserting scenarios and tasks on a touchscreen tab

关键词： 球形机器人   动力学   建模   运动特性  

摘要： 球形机器人是在传承传统机器人优势的基础上进行创新突破,现在已经是社会各界的广泛关注,尤其作为智能机器人的研究热点之一。但是鉴于当前研究球形机器人还不够深入,多少还存在很多不足和值得继续探索的地方,因此我们需要集思广益,加大研究力度。本文主要是基于动力学构建模型从而研究球形机器人的动力性能,文章在构建球形机器人的动力学模型进行仿真分析研究的时候,将球体接触面的摩擦力对球体所造成的影响也进行了考虑,分析了有无摩擦力时候的角速度和时间之间的关系,这也是值得深入和加强研究的地方,以便为球体机器人的深入探索打下坚实的基础,为智能机器人的研究领域提供更加创新的方式方法,为社会研究进步提供有力的支持。

关键词： Research Article  

摘要： The ability of today's robots to autonomously support humans in their daily activities is still limited. To improve this, predictive human-machine interfaces (HMIs) can be applied to better support future interaction between human and machine. To infer upcoming context-based behavior relevant brain states of the human have to be detected. This is achieved by brain reading (BR), a passive approach for single trial EEG analysis that makes use of supervised machine learning (ML) methods. In this work we propose that BR is able to detect concrete states of the interacting human. To support this, we show that BR detects patterns in the electroencephalogram (EEG) that can be related to event-related activity in the EEG like the P300, which are indicators of concrete states or brain processes like target recognition processes. Further, we improve the robustness and applicability of BR in application-oriented scenarios by identifying and combining most relevant training data for single trial classification and by applying classifier transfer. We show that training and testing, i.e., application of the classifier, can be carried out on different classes, if the samples of both classes miss a relevant pattern. Classifier transfer is important for the usage of BR in application scenarios, where only small amounts of training examples are available. Finally, we demonstrate a dual BR application in an experimental setup that requires similar behavior as performed during the teleoperation of a robotic arm. Here, target recognition processes and movement preparation processes are detected simultaneously. In summary, our findings contribute to the development of robust and stable predictive HMIs that enable the simultaneous support of different interaction behaviors.

关键词： Odor source localization   Neural controller   Evolutionary robotics   Continuous time recurrent neural network  

摘要： An Evolutionary Robotics (ER) approach to the task of odor source localization is investigated. In particular, Continuous Time Recurrent Neural Networks (CTRNNs) are evolved for odor source localization in simulated turbulent odor plumes. In the experiments, the simulated robot is equipped with a single chemical sensor and a wind direction sensor. Three main contributions are made. First, it is shown that the ER approach can be successfully applied to odor source localization in both low-turbulent and high-turbulent conditions. Second, it is demonstrated that a small neural network is able to successfully perform all three sub-tasks of odor source localization: (i) finding the odor plume, (ii) moving toward the odor source, and (iii) identifying the odor source. Third, the analysis of the evolved behaviors reveals two novel odor source localization strategies. These strategies are successfully re-implemented as finite state machines, validating the insights from the analysis of the neural controllers. (C) 2013 Elsevier B.V. All rights reserved.

摘要： Household robots need to perform tasks specific for the owner. With Learning from demon- stration (LfD) a robot can learn new tasks from human demonstrations, without requiring programming skills. This thesis investigates a novel representation of actions that can be learned by using only a 3d camera and an object tracker. The action representation is object- based so it is independent of the morphology of the robot. The actions are represented using the average and standard deviation of multiple demon- strated trajectories with six degrees of freedom. The standard deviation serves as a weight factor for the required accuracy of the recognized or synthesized trajectory. Three novel meth- ods proposed in this thesis aim to reduce variances in the demonstration that are not specific to the action. First the demonstrations are aligned in time using a novel action signature and a novel time warp algorithm. The time warp algorithm can approximate the alignment of multiple multidimensional signals in quadratic computing time. The third novel technique is a dynamically optimized choice of reference frame so variations in start and end position have little influence on the variance in trajectory. This method has been tested on a database of five actions repeatedly demonstrated by six subjects. The results show that it is possible to have a 90 percent action recognition rate with only three demonstrations in the database. It is also shown that a robot can use this action representation to synthesize four out of five actions with varying object positions.

关键词： 自动控制技术   刚-柔混合系统   并联连杆机构   动力学建模   越障机器人  

摘要： 针对越障机器人提出了一种刚-柔混合型并联连杆机构,建立了机构的系统模型。利用拉格朗日动力学方程以及模态方程建立了其动力学模型。对所建模型进行计算仿真,并与刚性系统进行了对比分析。分析结果表明:新模型的能量变化规律总体上与一般刚性系统的相似,因而能够适用于相同应用场合;在外力作用下,刚-柔混合系统的动能变化曲线更加平缓,具备了更强的缓解冲击的特性,因而更适用于存在较多冲击的越障情况。