Development of soft and distributed tactile sensors and the application to a humanoid robot

This paper describes a comprehensive tactile sensor system which can cover wide areas of full-body robots. Based on design criteria which are introduced from requirements, we develop two types of tactile sensor elements. One is a multi-valued touch sensor which has multi-level pressure thresholds. It is capable of covering wide areas of robot surfaces. The other is made of soft, conductive gel, which has the advantage of compliance compared with other sheet-type tactile sensors. With these two types sensors, we develop the tactile sensor system on the full-body robot 'H4'. Details of the sensor system on the robot and some experiments using tactile information are described.     

An active sensing strategy for contact location without tactile sensors using robot geometry and kinematics

In this study, we develop new techniques to sense contact locations and control robots in contact situations in order to enable articulated robotic systems to perform manipulations and grasping actions. Active sensing approaches are investigated by utilizing robot kinematics and geometry to improve upon existing sensing methods for contact. Compliant motion control is used so that a robot can actively search for and localize the desired contact location. Robot control in a contact situation is improved by the precise estimation of the contact location. From this viewpoint, we investigate a new control strategy to accommodate the proposed sensing techniques in contact situations. The proposed estimation algorithm and the control strategy both work complementarily. Then, we verify the proposed algorithm through experiments using 7-DOF hardware and a simulation environment. The two major contributions of the proposed active sensing strategy are the estimation algorithm for contact location without any tactile sensors, and the control strategy complementing the proposed estimation algorithm.     

Robot assembly of precision parts using tactile sensors

The general problem of including impulse effects in Lagrangian dynamics is discussed. A self-consistent method is developed and a corresponding block-diagram representation including initial conditions is constructed. An example of collision between two dissimilar robots is provided for illustration. A synthesis method for system control laws is derived from the Lagrangian. Application to control of impulse variations is discussed.     

基于JIT-RVC算法的软测量的研究及其应用

针对传统JIT(Just-in-time)算法单纯以距离为数据选择的缺陷,以及局部模型鲁棒性差,稳定性不强的特点,文中一方面提出了与Jolliffe参数相结合的鲁棒最近相关算法,使得JIT算法能够在离群点出现的情况下正确的选择建模数据,另一方面,还将合成随机变系数概率模型算法(Ensemble RVC)作为JIT算法的局部模型构成了JIT-RVC算法,于此强化了传统JIT算法的非线性逼近能力和鲁棒性。最后,通过强烈干扰情况下的污水生化处理的BOD_5软测量预测仿真结果证明了算法的有效性,特别要指出的是相较于JIT-PLS算法,JIT-RVC算法的RMSE指标减少了55%,而相关系数提高了53%。     

Slippage control in soft finger grasping and manipulation

Handling objects with robotic soft fingers without considering the odds of slippage are not realistic. Grasping and manipulation algorithms have to be tested under such conditions for evaluating their robustness. In this paper, a dynamic analysis of rigid object manipulation with slippage control is studied using a two-link finger with soft hemispherical tip. Dependency on contact forces applied by a soft finger while grasping a rigid object is examined experimentally. A power-law model combined with a linear viscous damper is used to model the elastic behavior and damping effect of the soft tip, respectively. In order to obtain precise dynamic equations governing the system, two second-order differential equations with variable coefficients have been designed to describe the different possible states of the contact forces accordingly. A controller is designed based on the rigid fingertip model using the concept of feedback linearization for each phase of the system dynamics. Numerical simulations are used to evaluate the performance of the controller. The results reveal that the designed controller shows acceptable performance for both soft and rigid finger manipulation in reducing and canceling slippage. Furthermore, simulations indicate that the applied force in the soft finger manipulation is considerably less than the rigid “one.”.     

Modeling and simulation of an artificial muscle and its application to biomimetic robot posture control

The shape memory effect exhibited by Nitinol wire can be utilized to construct an artificial muscle. The muscle is activated by an electric current, which produces heat and initiates a phase transformation. The Nitinol artificial muscle stress–strain–power relationship was determined by experiments, and a mathematical model was developed. The artificial muscle model was utilized for the posture control of a biomimetic underwater robot. The optimal activation patterns for height, pitch, and roll postures were determined. Simulation results for the height postures are in agreement with the experiments. The separation between the center of gravity and the centroid of the robot has a stabilizing effect on pitch and roll postures.     

一种时空协同的图卷积长短期记忆网络及其工业软测量应用

软测量技术的发展有效解决了工业过程中对于难以直接测量的质量变量的感知困难,为过程的控制与优化提供了有力保障.通常在含有多个质量变量的过程中,样本间的时序关系和多个质量变量间相互影响的空间关系能够反映过程本身的特性,这种时空特性的挖掘有益于软测量模型性能的提升,而传统软测量方法往往局限于对时序关系的学习而并未考虑对质量变量间的空间关系进行有效利用.对此,提出一种时空协同的图卷积长短期记忆网络(graph convolution long short-term memory networks, GC-LSTM),并应用于工业软测量场景.采用多通道网络结构将图卷积网络的空间关系挖掘能力与长短期记忆网络的时序关系学习能力相结合,对过程进行时空协同学习以实现软测量应用.具体而言,每条通道用于对每种质量变量进行独立学习;对于过程的时序特性,利用各通道内的长短期记忆网络提取针对不同质量变量的时序特征;对于过程的空间特性,构建质量变量间空间关系的图结构,采用跨通道的图卷积运算将不同通道内不同质量变量的时序特征基于空间关系进行融合,得到兼具过程时空特性的特征,从而在软测量建模中实现过程时空协同学习与融合...     

Electrostatic tactile display with thin film slider and its application to tactile telepresentation systems

A new electrostatic tactile display is proposed to realize compact tactile display devices that can be incorporated with virtual reality systems. The tactile display of this study consists of a thin conductive film slider with stator electrodes that excite electrostatic forces. Users of the device experience tactile texture sensations by moving the slider with their fingers. The display operates by applying two-phase cyclic voltage patterns to the electrodes. The display is incorporated into a tactile telepresentation system to realize explorations of remote surface textures with real-time tactile feedback. In the system, a PVDF tactile sensor and a DSP controller automatically generate voltage patterns to present surface texture sensations through the tactile display. A sensor, in synchronization with finger motion on the tactile display, scans a texture sample and outputs information about the sample surface. The information is processed by a DSP and fed back to the tactile display in real time. The tactile telepresentation system was evaluated in texture discrimination tests and demonstrated a 79 percent correct answer ratio. A transparent electrostatic tactile display is also reported in which the tactile display is combined with an LCD to realize a visual-tactile integrated display system.     

A servo-controlled robot gripper with multiple sensors and its logical specification

The logical specification of a microprocessor-based air-servo-controlled robot hand is presented, as well as its actual implementation. This hand can accommodate a wide variety of workpieces and allows for flexible assembly through the use of an automatic quick-change fingertip. The changeable set of gripper fingers is equipped with sensors, including a tactile force sensor, a crossfire sensor, a proximity sensor, and a slip sensor. A changeable set of gripper fingers with different sensing ranges can cope with certain subranges of the workpiece spectrum. A considerable cost saving is achieved by not changing the gripper itself. This specially designed hardware and software system includes position and force feedback. A PUMA 560 is used to test the success of the entire process.     

柔性触觉传感技术及其在医疗康复机器人的应用

柔性触觉传感器易于贴合皮肤等不规则表面,相比刚性传感器具有更强的信号感知能力、更高的精度和更佳的穿戴舒适性,在人机交互、医疗设备、可穿戴设备、健康监测等领域发挥着重要作用.鉴于此,从传感器不同工作原理出发,对柔性触觉传感器进行系统地介绍和对比,从结构优化的角度分析传感器性能优化方法,整理出微结构、结构疏松化、多模态测量...     

柔性触觉传感器在机器人上的应用综述

柔性多维触觉传感器的研究对于智能机器人的发展至关重要,研制符合实际应用要求的触觉传感器已经成为智能机器人发展中的关键技术之一.在分析国内外研究现状的基础上,阐述了触觉传感器应用在机器人上的主要技术,包括敏感材料选择、结构设计、解耦算法、信号获取电路、传感器标定以及虚拟触觉的研究等,为触觉传感器的研究提供方向.     

Human-following robot using the particle filter in ISpace with distributed vision sensors

We present a method for representing tracking and human-following by fusing distributed multiple vision systems in intelligent space, with applications to pedestrian tracking in a crowd. In this context, particle filters provide a robust tracking framework under ambiguous conditions. The particle filter technique is used in this work, but in order to reduce its computational complexity and increase its robustness, we propose to track the moving objects by generating hypotheses not in the image plan but on a top-view reconstruction of the scene. Comparative results on real video sequences show the advantage of our method for multiobject tracking. Simulations are carried out to evaluate the proposed performance. Also, the method is applied to the intelligent environment, and its performance is verified by experiments. This work was presented in part at the 10th International Symposium on Artificial Life and Robotics, Oita, Japan, February 4–6, 2005     

A strategy for fast grasping of unknown objects using partial shape information from range sensors

In this paper, we present a strategy for fast grasping of unknown objects based on the partial shape information from range sensors for a mobile robot with a parallel-jaw gripper. The proposed method can realize fast grasping of an unknown object without needing complete information of the object or learning from grasping experience. Information regarding the shape of the object is acquired by a 2D range sensor installed on the robot at an inclined angle to the ground. Features for determining the maximal contact area are extracted directly from the partial shape information of the unknown object to determine the candidate grasping points. Note that since the shape and mass are unknown before grasping, a successful and stable grasp cannot be in fact guaranteed. Thus, after performing a grasping trial, the mobile robot uses the 2D range sensor to judge whether the object can be lifted. If a grasping trial fails, the mobile robot will quickly find other candidate grasping points for another trial until a successful and stable grasp is realized. The proposed approach has been tested in experiments, which found that a mobile robot with a parallel-jaw gripper can successfully grasp a wide variety of objects using the proposed algorithm. The results illustrate the validity of the proposed algorithm in term of the grasping time.     

Interactive robot trajectory planning and simulation using Augmented Reality

Human-robot interaction in industrial robotics has largely been confined to finding better ways to reconfigure or program the robots. In this paper, an Augmented Reality based (RPAR-II) system is proposed to facilitate robot programming and trajectory planning considering the dynamic constraints of the robots. Through the various simulation capabilities provided in the proposed AR environment, the users are able to preview the simulated motion, perceive any possible overshoot, and resolve discrepancies between the planned and simulated paths prior to the execution of a task. By performing the simulation, the performance of the trajectory planning and the fitness of the selection of the robot controller model/parameters in the robot programming process can be visually evaluated. Practical issues concerning the system implementation are also discussed.     

Identification of a golf swing robot using soft computing approach

Golf swing robots have been recently developed in an attempt to simulate the ultra high-speed swing motions of golfers. Accurate identification of a golf swing robot is an important and challenging research topic, which has been regarded as a fundamental basis in the motion analysis and control of the robots. But there have been few studies conducted on the golf swing robot identification, and comparative analyses using different kinds of soft computing methodologies have not been found in the literature. This paper investigates the identification of a golf swing robot based on four kinds of soft computing methods, including feedforward neural networks (FFNN), dynamic recurrent neural networks (DRNN), fuzzy neural networks (FNN) and dynamic recurrent fuzzy neural networks (DRFNN). The performance comparison is evaluated based on three sets of swing trajectory data with different boundary conditions. The sensitivity of the results to the changes in system structure and learning rate is also investigated. The results suggest that both FNN and DRFNN can be used as a soft computing method to identify a golf robot more accurately than FFNN and DRNN, which can be used in the motion control of the robot.     

时间驱动仿真及其在宽带网络仿真中的应用

宽带网络的主要设备是宽带交换设备 ,如高性能路由器 ,交换机等 .因此 ,对宽带计算机网络的仿真 ,主要是对网络的宽带交换设备进行仿真 ,其关键是仿真速度问题 .本文针对计算机网络离散系统 ,提出了基于时间驱动的适合于离散系统的仿真算法 ,并与离散事件驱动仿真算法进行比较     

Progressive learning and its application to robot impedancelearning

An approach to learning control using an excitation scheduling technique is developed and applied to an impedance learning problem for fast robotic assembly. Traditional adaptive and learning controls incur instability depending on the reference inputs provided to the system. This technique avoids instability by progressively increasing the level of system excitation. Called progressive learning, it uses scheduled excitation inputs that allow the system to learn quasistatic parameters associated with slow input commands first, followed by the learning of dynamic parameters excited by fast input commands. As learning progresses, the system is exposed to a broader range of input excitation, which nonetheless does not incur instability and unwanted erratic responses. In robotic assembly, learning starts with a slow, quasistatic motion and goes to a fast, dynamic motion. During this process, the stiffness terms involved in the impedance controller are learned first, then the damping terms and finally by the inertial terms. The impedance learning problem is formulated as a model-based, gradient following reinforcement learning. The method allows the suppression of excessive parameter changes and thereby stabilizes learning. By gradually increasing the motion speed command, the internal model as well as the control parameters can be learned effectively within a focused, local area in the large parameter space, which is then gradually expanded as speed increases. Several strategies for motion speed scheduling are also addressed.     

Application of Heuristic Asymmetric Mapping for mobile robot navigation using ultrasonic sensors

In this paper, an experimental study of a navigation system that allows a mobile robot to travel in an environment about which it has no prior knowledge is described. Data from multiple ultrasonic range sensors are fused into a representation called Heuristic Asymmetric Mapping to deal with the problem of uncertainties in the raw sensory data caused mainly by the transducer's beam-opening angle and specular reflections. It features a fast data-refresh rate to handle a dynamic environment. Potential-field method is used for on-line path planning based on the constructed gridtype sonar map. The mobile robot can therefore learn to find a safe path according to its self-built sonar map. To solve the problem of local minima in conventional potential field method, a new type of potential function is formulated. This new method is simple and fast in execution using the concept from distance-transform path-finding algorithms. The developed navigation system has been tested on our experimental mobile robot to demonstrate its possible application in practical situations. Several interesting simulation and experimental results are presented.This work was supported partly by the National Science Council of Taiwan, ROC under the grant NSC-82-0422-E-009-321.     

卟啉和金属卟啉配合物的合成及其在传感器中的应用

气敏材料是气体（化学）传感器的核心部位,直接影响传感器的稳定性、选择性、灵敏度和响应时间等各种性能。卟啉与金属卟啉配合物具有优良的气敏性能,目前国内外卟啉与金属卟啉传感器已应用于VOCs的检测。该文介绍了卟啉及其结构、合成方法、卟啉和金属卟啉配合物的合成及影响因素;卟啉和金属卟啉在传感器中的应用和对挥发性有机气体的检测原理。     

Development and industrial application of soft sensors with on-line Bayesian model updating strategy

This paper deals with the issues associated with the development of data-driven models as well as model update strategy for soft sensor applications. A practical yet effective solution is proposed. Key process variables that are difficult to measure are commonly encountered in practice due to limitations of measurement techniques. Even with appropriate instruments, some measurements are only available through off-line laboratory analysis with typical sampling intervals of several hours. Soft sensors are inferential models that can provide continuous on-line prediction of hidden variables; such models are capable of combining real-time measurements with off-line lab data. Due to the prevalence of plant-model mismatch, it is important to update the model using the latest reference data. In this paper, parameters of data-driven models are estimated using particle filters under the framework of expectation–maximization (EM) algorithms. A Bayesian methodology for model calibration strategy is formulated. The proposed framework for soft sensor development is applied to an industrial process to provide on-line prediction of a quality variable.