Walking Pattern Generation for a Biped Walking Robot Using Convolution Sum

This paper describes a novel walking pattern generation method for a biped humanoid robot using a convolution sum. For a biped walking model, a single mass inverted pendulum model is generally used and the zero moment point (ZMP) equation is described by a decoupled linear differential equation. As a walking pattern generation method for the robot model, a novel method using a convolution sum is proposed in this paper. From the viewpoint of the linear system response, walking pattern generation can be regarded as a convolution of an arbitrary reference ZMP and the walking pattern for an impulse reference ZMP. For the calculation of convolution, the walking pattern for an impulse reference ZMP is first derived from the analytic walking pattern for a step reference ZMP. The convolution sum is then derived in two recursive forms, which can be applied online and offline, respectively. The proposed algorithm requires low computation power, since the walking pattern equation is composed of a recursive form. As the algorithm is expressed in analytic form, it is not necessary to solve optimization problems or calculate the fast Fourier transform, contrary to previous approaches. A computer simulation of walking demonstrates that the proposed algorithm yields excellent accuracy compared to the preview control method — one of the most highly regarded walking pattern generation methods. In addition, the application on the multi-point mass model is shown with the computer simulation.     

Error Analysis and Effective Adjustment of the Walking-Ready Posture for a Biped Humanoid Robot

A walking control algorithm is generally a mixture of various controllers; it depends on the characteristics of the target system. Simply adopting one part of another researcher's algorithm does not guarantee an improvement in walking performance. However, this paper proposes an effective algorithm that can be easily adopted to other biped humanoid robots; the algorithm enhances the walking performance and stability of the robot merely by adjusting the walking-ready posture. The walking performance of biped humanoid robots is easily affected by an unsuitable walking-ready posture in terms of accuracy and repeatability. More specifically, low accuracy for the walking-ready posture may cause a large difference between an actual biped robot and its mathematical model, and the low repeatability may disturb the evaluation of the performances of balance controllers. Therefore, this paper first discusses the factors that detrimentally affect bipedal walking performance and their phenomena in the walking-ready posture. The necessary conditions for an ideal walking-ready posture are then defined based on static equilibrium and a suitable adjustment algorithm is proposed. Finally, the effectiveness of the algorithm is verified through dynamic computer simulations.     

Whole Body Motion Noise Cancellation of a Robot for Improved Automatic Speech Recognition

The motors of a robot produce ego-motion noise that degrades the quality of recorded sounds. This paper describes an architecture that enhances the capability of a robot to perform automatic speech recognition (ASR) even as the entire body of the robot moves. The architecture consists of three blocks: (i) a multichannel noise reduction block, consisting of microphone-array-based sound localization, geometric source separation and post-filtering, (ii) a single-channel template subtraction block and (iii) an ASR block. As the first step of our analysis strategy, we divided the whole-body motion noise problem into three subdomains of arm, leg and head motion noise, according to their intensity levels and spatial location. Subsequently, by following a synthesis-by-analysis approach, we determined the best method for suppressing each type of ego-motion noise. Finally, we proposed to utilize a control module in our ASR framework; this module was designed to make decisions based on instantaneously detected motions, allowing it to switch to the most appropriate method for the current type of noise. This proposed system resulted in improvements of up to 50 points in word correct rates compared with results obtained by single microphone recognition of arm, leg and head motions.     

Construction of a Gait Adaptation Model in Human Split-Belt Treadmill Walking Using a Two-Dimensional Biped Robot

A number of studies have measured kinematics, dynamics and oxygen uptake while a person walks on a treadmill. In particular, during walking on a split-belt treadmill, in which the left and right belts have different speeds, remarkable differences in kinematics are observed between normal subjects and subjects with cerebellar disease. In order to construct a gait adaptation model of such human split-belt treadmill walking, we proposed a simple control model and developed a new two-dimensional biped robot walk on a split-belt treadmill. We combined the conventional limit-cycle-based control consisting of joint PD control, cyclic motion trajectory planning and a stepping reflex with a newly proposed adjustment of P-gain at the hip joint of the stance leg. The data obtained in experiments on the robot (normal subject model and cerebellum disease subject model) have highly similar ratios and patterns to data obtained in experiments on normal subjects and subjects with cerebellar disease carried out by Bastian et al. We also showed that the P-gain at the hip joint of the stance leg was the control parameter of adaptation for symmetric gaits in split-belt walking and that P-gain adjustment corresponded to muscle stiffness adjustment by the cerebellum. Consequently, we successfully proposed a gait adaptation model for human split-belt treadmill walking, and confirmed the validity of our hypotheses and the proposed model using the biped robot.     

Pose Control of a Lake Surface Cleaning Robot Using Backstepping and Polar Coordinates

In this paper, the stabilization control problem of a lake surface cleaning robot (LSCR) that is driven by a driving and steering mechanism is addressed. Since the LSCR has more degrees of freedom than the number of control inputs, its motion is subject to non-holonomic constraints. Generally, this kind of system cannot be asymptotically stabilized using a time-invariant smooth feedback controller in the Cartesian coordinates. A novel controller using the vector backstepping technique for controlling the position and orientation of the LSCR is presented. We first represent the pose of the LSCR by a polar coordinate system centered at the desired pose and transform the dynamics equation of the LSCR from the Cartesian coordinates to the polar coordinates. Then a feedback control law is derived to yield global asymptotic convergence of the position and orientation of the system to the desired values using the vectorial backstepping design scheme. We prove the asymptotic stability of the system under the control of the proposed method using the Lyapunov theory. Simulations have been carried out to demonstrate the performance of the proposed controller.     

General Feature Extraction for Mapping and Localization of a Mobile Robot Using Sparsely Sampled Sonar Data

This study introduces a method of general feature extraction for building a map and localization of a mobile robot using only sparsely sampled sonar data. Sonar data are acquired by using a general fixed-type sensor ring that frequently provides false returns on the locations of objects. We first suggest a data association filter that can classify sets of sonar data that are associated with the same hypothesized feature into one group. A feature extraction method is then introduced to decide the exact geometric parameters of the hypothesized feature in the group. We also show the possibility of extracting a circle feature consistently as well as a line or a point feature by using the proposed filter. These features are then assembled to build a global map and applied to extended Kalman filter-based localization of the robot. We demonstrate the validity of the proposed filter with the results of mapping and localization produced by real experiments.     

Development of a Face Verification System for a Home Service Robot

The robot providing services based on interaction with a human needs a fast verification process that knows whether the user belongs to a family or a guest group for providing the differentiated services to each group member. In this paper, we developed a verification system for one-to-group matching (e.g., user-to-family or user-to-guest) and tested on the database acquired using the robot in an uncontrolled environment. The proposed system consists of three parts: face detection using revised modified census transform and adaboost, feature extraction using multiple principle component analysis, and verification using the aggregating Gaussian mixture model–universal background model. Experimental results show that the proposed system works well on the deteriorated database with a high speed.     

Implementation of a Variable D-H Parameter Model for Robot Calibration Using an FCMAC Learning Algorithm

Current robot calibration schemes usually employ calibration models with constant error parameters. Consequently,they are inevitably subject to a certain degree of locality, i.e., the calibrated error parameters (CEPs) will produce the desiredaccuracy only in certain regions of the robot workspace. To deal with the locality phenomenon, CEPs that vary in differentregions of the robot workspace may be more appropriate. Hence, we propose a variable D-H (Denavit and Hartenberg)parameter model to formulate variations of CEPs. An FCMAC (Fuzzy Cerebellar Model Articulation Controller) learningalgorithm is used to implement the proposed variable D-H parameter model. Simulations and experiments that verify theeffectiveness of the proposed calibration scheme based on the variable D-H parameter model are described.     

Development of a Novel Live-Line Inspection Robot System for Post Insulators at 220-kV Substations

Based on investigating existing on-line detection methods for energized ceramic post insulators, this paper proposes a new live-line work robot involving ultrasonic detection for flaws in porcelain post insulators at 220-kV high-voltage (HV) substations. Owing to the HV, strong electromagnetic field and space conditions, the robot would be a special one. Thus, on the basis of studying correlative technologies — mechanism design, visual alignment and location, HV insulation, electromagnetic compatibility, automatic control, and wireless communication — each part of the robot system is designed and analyzed. The experiment and trial operation results show that the designed robot can work normally in 220-kV substations, and cracks in porcelain post insulators can be detected effectively, safely and conveniently by the robot.     

Development of a Positioning Technique for an Urban Area Using Omnidirectional Infrared Camera and Aerial Survey Data

This paper describes an outdoor positioning system for vehicles that can be applied to an urban canyon by using an omnidirectional infrared (IR) camera and a digital surface model (DSM). By means of omnidirectional IR images, this system enables robust positioning in urban areas where satellite invisibility caused by buildings hampers high-precision GPS measurements. The omnidirectional IR camera can generate IR images with an elevation of 20–70° for the surrounding area of 360°. The image captured by the camera is highly robust to light disturbances in the outdoor environment. Through the IR camera, the sky appears distinctively dark; this enables easy detection of the border between the sky and the buildings captured in white due to the difference in the atmospheric transmittance rate between visible light and IR rays. The omnidirectional image, which includes several building profiles, is compared with building-restoration images produced by the corresponding DSM in order to determine the self-position. Field experiments in an urban area show that the proposed outdoor positioning method is valid and effective, even if high-rise buildings cause satellite blockage that affects GPS measurements.     

TORSO: Development of a Telexistence Visual System Using a 6-d.o.f. Robot Head

In telexistence master–slave systems, it is important to transmit visual information from remote places to the operator. Conventional imaging devices in head-mounted displays (HMDs) can only express the three-axis rotation of the neck. However, humans can obtain broader visual fields and motion parallax information from the translational motion of their necks. We have proposed a system that can acquire natural and comfortable visual information, and can accurately track the head motion of a person. Our proposed device can express the head motion and the translation movements of the neck. We have developed a robot, called 'TORSO', and constructed a telexistence visual system with a display device, HMD. In this paper, by means of a broader field of view achieved by motion involving looking around, we demonstrate the advantage and novelty of our proposed system. In addition, we suggest the evolution of the TORSO–HMD system.     

Bi-criteria Velocity Minimization of Robot Manipulators Using a Linear Variational Inequalities-Based Primal-Dual Neural Network and PUMA560 Example

In this paper, to diminish discontinuity points arising in the infinity-norm velocity minimization scheme, a bi-criteria velocity minimization scheme is presented based on a new neural network solver (i.e., a primal-dual neural network based on linear variational inequalities (LVI)). Such a kinematic control scheme of redundant manipulators can incorporate joint physical limits such as joint limits and joint velocity limits simultaneously. Moreover, the presented kinematic control scheme can be formulated as a quadratic programming (QP) problem. As a real-time QP solver, the LVI-based primal-dual neural network is established with a simple piecewise linear structure and higher computational efficiency. Computer simulations performed based on a PUMA560 manipulator are presented to illustrate the validity and advantages of such a bi-criteria neural control scheme for redundant robots.     

Development of a Hybrid Wheel-Legged Mobile Robot WR-3 Designed for the Behavior Analysis of Rats

This paper presents the design and development of a bio-inspired mobile robot called WR-3 (Waseda Rat no. 3). The purpose of the robot is to work as an experimental tool to study social interaction between rats and robots. According to the results of the analysis of the motion of rats, their behavior can be divided into two phases: movement and interaction. Therefore, a novel hybrid mechanism that uses wheels during the movement phase and legs while interacting has been designed to actuate WR-3. Consequently, the robot can move at a high speed using its wheels, and reproduce the rat's interaction behavior using its legs and other parts. Based on body structure of a mature rat, WR-3 has been designed with similar dimensions and shape as a mature rat, and the quality of the shape imitation has been verified by the experiments of a rat's interestingness to the robot and stuffed rat. Evaluation experiments show that WR-3 is capable of reproducing a rat's actions such as chasing, rearing, grooming, mounting, etc., similar to a real rat. Furthermore, preliminary social interaction tests with living rats reveal that WR-3 is to some extent able to evoke natural reactions form a real rat and is therefore able to perform a certain level of realistic interaction.     

Mechanical Design of a Trunk with Redundant and Viscoelastic Joints for Rhythmic Quadruped Locomotion

Passive mechanisms, such as free joints and viscoelastic components, enable natural oscillation of the robot body, which allows rhythmic locomotion with low energy and computational costs. In particular, joint viscoelasticity can be a powerful candidate for changing natural oscillation and so influence the operation performance of locomotion. The present study considers the passive mechanism of a trunk, and investigates the contributions of a trunk mechanism with redundant joints and tunable viscoelasticity to quadruped locomotion. A physical quadruped robot with a trunk mechanism is developed, and the walking performance of this robot for various gait patterns and joint viscoelasticities is investigated. A simulation model is also constructed based on the physical robot, and the contribution of the viscoelasticity to trunk oscillation and the appropriate joint viscoelasticity and number of trunk joints are discussed. Experimental results obtained using the physical robot indicate that the proposed trunk mechanism contributes to successful locomotion as compared to a robot with a rigid trunk and that the velocity is influenced by not only the gait pattern, but also the joint viscoelasticity (i.e., there are appropriate couplings of the joint viscoelasticity and gait pattern). The simulation results indicate that the trunk mechanism requires joint viscoelasticity in order to achieve oscillation and that a greater number of joints having a smaller joint viscoelasticity enables higher velocity. These results suggest that, in addition to the leg mechanism and the controller design, the design of the trunk mechanism is also important.     

A Neural Network Technique of Compensating for an Inertia Model Error in a Time-delayed Controller for Robot Manipulators

International Journal of Control, Automation and Systems - A time-delayed control (TDC) method is known as a simple, robust and non model-based control scheme that requires the fast sampling time,...     

用快速EM算法实现小波系数的高斯混合分布模型

提出了一种小波系数的高斯混合分布模型。该模型用两个分量的高斯混合分布来拟合小波域每个高频频带系数的分布:其中,先验概率小方差大的分布代表了少量幅值较大的小波系数;先验概率大方差小的分布代表了大量幅值较小的小波系数。该文采用基于特征的快速算法完成对高斯混合分布参数的求解。实验结果证明了模型的有效性。     

工业电加热炉时滞状态空间模型的辨识

电加热炉是工业生产过程中极为重要且常见的设备，其建模与高精度控制是极为困难的。本文研究一种时滞状态空间模型的辨识方法，根据采集的电加热炉的工业数据得到其时滞状态空间模型，并比较不同滞后效应的模型及其拟合精度，给出了极为满意的结果。     

基于人工神经网络的固体氧化物燃料电池性能 预测模型开发

固体氧化物燃料电池(Solid-Oxide Fuel Cell,SOFC)因其能量转换效率高而备受关注,但其相 关技术非常复杂,技术成熟度比质子交换膜燃料电池、直接甲醇燃料电池等其他类型的燃料电池低。 SOFC 的微观结构是影响其性能的因素之一,为加速 SOFC 的商业化应用,需要对其复杂微观结构进 行有效优化。同时,SOFC 性能测试实验耗时长、费用高,而高可靠性的 SOFC 计算机模型可用来缩 短 SOFC 微观结构优化时间和降低研发成本。该研究根据阳极支撑 SOFC 结构变化对应的性能实验 数据,开发了一种基于人工神经网络的、根据结构特性来预测其性能的 SOFC 计算机模型。实验过程 利用部分数据对该人工神经网络进行训练,并利用另一部分数据对其进行验证。结果显示,所开发的 SOFC 模型能够准确地根据微观结构的变化呈现其性能变化,适合用于 SOFC 微观结构的优化。