关键词： Autonomous underwater vehicles  

摘要： A recursive subspace identification algorithm for autonomous underwater vehicles (AUVs) is proposed in this paper. Due to the advantages at handling nonlinearities and couplings, the AUV model investigated here is for the first time constructed as a Hammerstein model with nonlinear feedback in the linear part. To better take the environment and sensor noises into consideration, the identification problem is concerned as an errors-in-variables (EIV) one which means that the identification procedure is under general noise assumption. In order to make the algorithm recursively, propagator method (PM) based subspace approach is extended into EIV framework to form the recursive identification method called PM-EIV algorithm. With several identification experiments carried out by the AUV simulation platform, the proposed algorithm demonstrates its effectiveness and feasibility.

关键词： Statistics  

摘要： Navigation systems of autonomous vehicles often exploit range measurement information that may be affected by outliers. In marine application the presence of outliers in sonar bathymetry, for instance, can be particularly severe due to multipath phenomena in the acoustic propagation. This paper describes a possible approach to process range measurements highly contaminated by outliers. The proposed solution builds on a robust parameter identification algorithm minimizing a nonlinear cost function that exploits the mathematical properties of Gibbs entropy. Numerical examples on simulated data are provided to illustrate the method and its performance. The use of simulated data allows to vary the amount of noise and outliers contamination while knowing the ground truth values of the parameters to be identified. For the sake of experimental validation, the method is also applied to third party (publicly available) upward looking sonar ice draft data collected by submarines in the Arctic Ocean. © 2013 De Gruyter. All rights reserved.

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.

关键词： 七自由度   加速度突变   动力学  

摘要： 对七自由度机器人动力学性能进行研究并优化,七自由度机器人结构上具有冗余关节,可以进行容错控制,因此容错时可能会产生关节的加速度突变,从而引起系统故障甚至损坏机器人系统。为防止加速度突变引起机器人运动故障,首先进行机器人动力学建模及分析,在此基础上将求解出的加速度优化处理,将优化后的加速度进行最优化分析,有效克服了高速运动及容错时容易发生的关节加速度突变。通过实验仿真观测到该优化可以对关节角度实现快速准确的跟踪。

关键词： 并联机器人   虚功原理   奇异值分解  

摘要： 尺度参数优化是并联机器人设计的最终目标。对研制的Delta并联机器人进行动力学分析,基于虚功原理,建立Delta并联机器人系统的动力学方程,利用奇异值分解理论,以在末端单位加速度下单轴最大输出扭矩在整个工作空间的最大值最小的动力学性能指标作为评价指标,从而获得满足设计需求的一组最优尺度参数。

关键词： 极限学习机   逆动力学模型   机器人   RBF神经网络   系统建模  

摘要： 极限学习机(extreme learning machine,ELM)是一种新型的前馈神经网络,可有效的处理复杂的非线性的回归拟合问题。本文提出了基于极限学习机的机器人逆动力学模型的建模方法,阐述了基本的设计思想和具体的算法过程,通过对二自由度刚性机器人的仿真,结果验证了该方法的有效性和可行性,与RBF神经网络相比极限学习机的优点在于训练时间短,能极快的达到全局收敛,且非线性系统建模精度和模型泛化能力都得到提高。

关键词： 并联机器人   动力学性能   优化设计   全域工作空间   影响系数  

摘要： 为优化一种风洞6_PUS并联支撑机器人的动力学性能参数,采用螺旋理论推导了该机构的力雅可比矩阵和Hessian矩阵,并基于一、二阶影响系数法,在全域工作空间内,对影响6_PUS风洞并联支撑机器人动力学性能指标的主要结构参数进行了优化分析.对该机器人动平台、球铰位置、拉杆长度、平行梁宽度等参数对动力学性能的影响进行了仿真.从仿真结果来看,6_PUS并联机器人的动力学性能较差,要使机器人获得最佳的综合性能,则需要对这些参数进行综合优化.通过对这些参数和性能关系之间的全面考虑,最后选定了并联机器人的主要设计参数,使机构性能达到了设计要求.

关键词： SCARA机器人   动力学模型   拉格朗日动力学公式   动力学仿真  

摘要： 以SCARA型四自由度机器人为研究对象,采用Denavit-Hartenberg方法建立SCARA机器人的运动学模型,重点使用拉格朗日公式法对SCARA机器人的动力学模型进行了详细地推导,并得出SCARA机器人的动力学方程。同时归纳使用拉格朗日公式法推导机器人动力学模型的一般方法,该方法适用于其他构型机器人的动力学建模。最后对机械臂动力学方程的一般形式进行解析。使用拉格朗日公式法获得准确的机器人动力学模型可以为机器人的结构设计、关键件的选型以及控制器的设计和仿真提供依据。