Online generation and control of quasi-static multi-contact motion by PWT Jacobian matrix with contact wrench estimation and joint load reduction*

In this paper, we propose a generic method of online motion generation and control that realizes quasi-static multi-contact motion for real position-controlled humanoids. The proposed system calculates command joint angle online by prioritized inverse kinematics that realizes the target contact states and the target position/orientation of interaction end-effectors feasibly. In order to enable control of contact wrench and joint torque with position-controlled robots, Jacobian matrixes (PWT Jacobian matrix) focusing on relationships between command joint angle and actual joint position, contact wrench, and joint torque are introduced. In addition, contact wrench is estimated at the body parts where force sensors are not mounted to enable contacts there, and joint load reduction based on the motor temperature is taken into account to enable long-term motions with real humanoids. The proposed method was verified by a real life-size position-controlled humanoid HRP2-JSKNTS, and various multi-contact motions such as desk climbing using the right knee and both arms were realized.     

Sensor information analysis for a humanoid robot

For a humanoid robot to safely walk in unknown environments, various sensors are used to identify the surface condition and recognize any obstacles. The humanoid robot is not fixed on the surface and the base/orientation of the kinematics change while it is walking. Therefore, if the foot contact changes from the estimated due to the unknown surface condition, the kinematics results are not correct. The robot may not be able to perform the motion commands based on the incorrect surface condition. Some robots have built-in range sensors but it’s difficult to accurately model the surface from the sensor readings because the movement of the robot should be considered and the robot localization should have zero error for correct interpretation of the sensor readings. In this paper, three infrared range sensors are used in order to perceive the floor state. Covariance analysis is incorporated to consider the uncertainties. The accelerometer and gyro sensor are also used in order to detect the moment a foot hits the surface. This information provides correction to the motion planner and robot kinematics when the environment is not modeled correctly.     

Humanoids skill learning based on real-time human motion imitation using Kinect

In this paper, a novel framework which enables humanoid robots to learn new skills from demonstration is proposed. The proposed framework makes use of real-time human motion imitation module as a demonstration interface for providing the desired motion to the learning module in an efficient and user-friendly way. This interface overcomes many problems of the currently used interfaces like direct motion recording, kinesthetic teaching, and immersive teleoperation. This method gives the human demonstrator the ability to control almost all body parts of the humanoid robot in real time (including hand shape and orientation which are essential to perform object grasping). The humanoid robot is controlled remotely and without using any sophisticated haptic devices, where it depends only on an inexpensive Kinect sensor and two additional force sensors. To the best of our knowledge, this is the first time for Kinect sensor to be used in estimating hand shape and orientation for object grasping within the field of real-time human motion imitation. Then, the observed motions are projected onto a latent space using Gaussian process latent variable model to extract the relevant features. These relevant features are then used to train regression models through the variational heteroscedastic Gaussian process regression algorithm which is proved to be a very accurate and very fast regression algorithm. Our proposed framework is validated using different activities concerned with both human upper and lower body parts and object grasping also.     

Measurement and comparison of humanoid H7 walking with human being

This paper describes our research efforts aimed at understanding human being walking functions. Using a motion-capture system, force plates and distributed force sensors, walk motion of both human being and humanoid H7 was captured. Experimental results are shown. Comparisons in between human being and H7 walk made using the following characteristics: (1) ZMP trajectories; (2) torso movement; (3) free leg trajectories; (4) joint angle usage; (5) joint torque usage. Furthermore, implications of the comparisons to the humanoid robot are discussed.     

Correlation-Based Scan Matching Using Ultrasonic Sensors for EKF Localization

This paper presents a localization method for a mobile robot equipped with only low-cost ultrasonic sensors. Correlation-based Hough scan matching was used to obtain the robot’s pose without any predefined geometric features. A local grid map and a sound pressure model of ultrasonic sensors were used to acquire reliable scan results from uncertain and noisy ultrasonic sensor data. The robot’s pose was measured using correlation-based Hough scan matching, and the covariance was calculated. Localization was achieved by fusing the measurements from scan matching with the robot’s motion model through the extended Kalman filter. Experimental results verified the performance of the proposed localization method in a real home environment.     

Motion teaching method for complex robot links using motor current

Conventional robot motion teaching methods use a teaching pendant or a motion capture device and are not the most convenient or intuitive ways to teach a robot sophisticated and fluid movements such as martial arts motions. Ideally, a robot could be set up as if it were a clothing mannequin that has light limbs and flexible yet frictional joints which can be positioned at desirable shape and hold all the positions. To do the same with a robot, an operator could pull or push the links with minor forces until the desired robot posture is attained. For this, a robot should measure the applied external force by using torque sensors at the robot joints. However, torque sensors are bulky and expensive to install in every DOF joints while keeping a compact design, which is essential to humanoid robots. In this paper, we use only motor current readings to acquire joint torques. The equations used to compensate for the effect of gravity on the joint torques and the self-calibration method to earn link parameters are presented. Additionally, kinematic restrictions can be imposed on the robot’s arms to simplify the motion teaching. Here, we teach the Kendo training robot with this method and the robot’s learnt martial art motions are demonstrated.     

基于多传感器集成的仿人机器人足部感知系统

足部是仿人机器人本体支撑的基础,也是唯一与地面接触并发生相互作用的主要部件,其各种地面信息获取能力是机器人实现仿人的自然性稳定行走控制的关键.基于六维力传感器、惯量测量单元和柔性触觉阵列传感器,设计了一种新型仿人机器人集成化足部感知系统(IPFS).具备对各种地面环境识别和足部姿态获取、足底与外界接触位置的实时感知和估...     

ADXL203型双轴加速计在机器人足部感知系统中的应用

仿人机器人要实现在复杂环境下稳定行走,仅仅依靠地面反力信息远不能满足应用要求,此时足部的倾角信息显得更为重要。脚面倾角可以反映地面倾斜状态,是仿人机器人稳定控制的一个重要依据。利用ADXL203双轴加速度传感器与DSP(TMS3202811)实现对倾角信息的实时高速采集与处理,并通过实验证明了倾角传感器在机器人足部感知系统中是可行的。     

双目视觉辅助的激光惯导SLAM算法

激光同步定位与地图构建(simultaneous localization and mapping, SLAM)算法在位姿估计和构建环境地图时依赖环境结构特征信息,在结构特征缺乏的场景下,此类算法的位姿估计精度与鲁棒性将下降甚至运行失败.对此,结合惯性测量单元(inertial measurement unit, IMU)不受环境约束、相机依赖视觉纹理的特点,提出一种双目视觉辅助的激光惯导SLAM算法,以解决纯激光SLAM算法在环境结构特征缺乏时的退化问题.即采用双目视觉惯导里程计算法为激光扫描匹配模块提供视觉先验位姿,并进一步兼顾视觉约束与激光结构特征约束进行联合位姿估计.此外,提出一种互补滤波算法与因子图优化求解的组合策略,完成激光里程计参考系与惯性参考系对准,并基于因子图将激光位姿与IMU数据融合以约束IMU偏置,在视觉里程计失效的情况下为激光扫描匹配提供候补的相对位姿预测.为进一步提高全局轨迹估计精度,提出基于迭代最近点匹配算法(iterative closest point, ICP)与基于图像特征匹配算法融合的混合闭环检测策略,利用6自由度位姿图优化方法显著降低里程计漂移误...     

Highly Precise Dynamic Simulation Environment for Humanoid Robots

In this paper we present a simulation environment for humanoid robots with a precise and efficient method of handling ground contact, and experiments empirically validating the simulator. Highly accurate dynamic simulation is an essential tool for research and development in humanoid robotics, and a simulator should ideally provide a transparent interface with pathways for control and sensing information identical to those of the actual robot(s) it models. We identified ground contact as the chief source of divergence from reality in work to date and have tackled this problem by developing an algorithm for resolving ground contact for humanoid robots. Our objective was to produce an algorithm that is accurate, efficient and easy to implement. The algorithm is general with respect to the complexity of the foot model; is based on empirically measurable characteristics of the foot–ground interaction, i.e., friction, which we have obtained using experiments described; provides an exact implementation of the Coulomb friction model (avoiding polyhedral approximation of the friction cone); runs in real-time; is also amenable to a straightforward accuracy–speed trade-off; and is relatively easy to implement as a constraint selection method. The simulation environment embodies generality, and we have applied it to two different humanoid robots, Hoap-2 and CB. We present experiments comparing the results of simulation with identical motions performed by real robots, and comparing the full contact resolution algorithm, the modification trading accuracy for computational speed and a penalty-based method.     

A body model server for human motion capture and representation

This paper presents a body model server (BMS) that provides real-time access to the position and posture of a person's torso, arms, hands, head, and eyes. It can be accessed by clients over a network. The BMS is designed to function as a device-independent data-layer between the sensing devices and client applications that require real-time human motion data, such as animation control. It can provide clients with accurate information at up to 40 Hz. For data collection, the model uses four magnetic position/orientation sensors, two data-gloves, and an eye-tracker. The BMS combines the data-streams from the sensors and transforms them into snapshots of the user's upper-body pose. A geometric model made up of joints and segments structures the input. Posture of the body is represented by joint angles. Two unique characteristics of our approach are the use of the implicit, geometric constraints of the sensed body to simplify the computation of the unmeasured joint angles, and the use of time-stamped data that allow synchronization with other data streams, e.g., speech input. This paper describes the architecture of the BMS, including the management of multiple input devices, the representation and computation of the position and joint angle data, and the client-server interface.     

A sensor-aided self coaching model for uncocking improvement in golf swing

This paper describes an autonomous kinematic analysis platform for wrist angle measurement that is capable of evaluating a user’s uncocking motion in his or her golf swing and providing instructional multimodal feedback to improve his or her skills. This uncocking motion, which is a characteristic movement of the wrist during the golf swing, is an important factor in achieving accurate ball hitting and long driving distances, but is difficult to measure. In order to efficiently compute the wrist angle for uncocking evaluation, we present a sensor-based intelligent Inertial Measurement Unit (IMU) agent that collects three-dimensional orientation data during the golf swing from two IMU sensors placed on the forearm and on the golf club. It accurately analyzes changes in wrist angle to detect uncocking throughout the sequence of golf swing motions. In this paper, we first introduce the design considerations based on the concept of the uncocking motion and explain the system architecture with the sensors used for quantitative measurement and qualitative feedback generation. Then, we illustrate the detailed algorithms for wrist angle computation, golf swing motion segmentation based on key pose detection, and uncocking evaluation. A multimodal feedback-based user interface for our system is also presented. Experimental results show that the proposed system has the ability to accurately calculate the wrist angle in real time and also that it can be applied to a practical self-coaching system to improve the uncocking motion.     

Weighted hierarchical quadratic programming: assigning individual joint weights for each task priority

This paper proposes a novel method that computes the optimal solution of the weighted hierarchical optimization problem for both equality and inequality tasks. The method is developed to resolve the redundancy of robots with a large number of Degrees of Freedom (DoFs), such as a mobile manipulator or a humanoid, so that they can execute multiple tasks with differently weighted joint motion for each priority level. The proposed method incorporates the weighting matrix into the first-order optimality condition of the optimization problem and leverages an active-set method to handle equality and inequality constraints. In addition, it is computationally efficient because the solution is calculated in a weighted joint space with symmetric null-space projection matrices for propagating recursively to a low priority task. Consequently, robots that utilize the proposed method effectively show whole-body motions handling prioritized tasks with differently weighted joint spaces. The effectiveness of the proposed method was validated through experiments with a nonholonomic mobile manipulator as well as a humanoid.

     

Multisensor Input for CPG-Based Sensory---Motor Coordination

This paper describes a method for providing in real time a reliable synchronization signal for cyclical motions such as steady-state walking. The approach consists in estimating online a phase variable on the basis of several implicit central pattern generator associated with a set of sensors. These sensors can be of any kind, provided their output strongly reflects the timed motion of a link. They can be, for example, spatial position or orientation sensors, or foot sole pressure sensors. The principle of the method is to use their outputs as inputs to nonlinear observers of modified Van der Pol oscillators that provide us with several independent estimations of the overall phase of the system. These estimations are then combined within a dynamical filter constituted of a Hopf oscillator. The resulting phase is a reliable indexing of the cyclic behavior of the system, which can finally be used as input to low-level controllers of a robot. Some results illustrate the efficiency of the approach, which can be used to control robots.     

基于惯性/磁力传感器与单目视觉融合的SLAM方法

针对单目视觉SLAM（同时定位与地图构建）算法没有尺度信息以及在相机移动过快时无法使用的问题,提出了一种IMU（惯性测量单元）!!/磁力传感器与单目视觉融合的SLAM方法．首先,提出了一种模糊自适应的九轴姿态融合算法,对IMU的航向角进行高精度估计．然后,采用单目ORB-SLAM2（oriented FAST and rotated BRIEF SLAM2）算法,通过IMU估计其尺度因子,并对其输出的位姿信息进行尺度转换．最后,采用松耦合方式,对IMU估计的位姿和ORB-SLAM2算法经过尺度转换后的位姿,进行卡尔曼滤波融合．在公开数据集EuRoC上进行了测试,测试结果表明本文方法总的位置均方根误差为5.73 cm．为了进一步在实际环境中验证,设计了全向移动平台,以平台上激光雷达所测的位姿数据为基准,测试结果表明本文方法的旋转角度误差小于5°,总的位置均方根误差为9.76 cm．     

Laser-Based Tracking of Human Position and Orientation Using Parametric Shape Modeling

Robots designed to interact socially with people require reliable estimates of human position and motion. Additional pose data such as body orientation may enable a robot to interact more effectively by providing a basis for inferring contextual social information such as people's intentions and relationships. To this end, we have developed a system for simultaneously tracking the position and body orientation of many people, using a network of laser range finders mounted at torso height. An individual particle filter is used to track the position and velocity of each human, and a parametric shape model representing the person's cross-sectional contour is fit to the observed data at each step. We demonstrate the system's tracking accuracy quantitatively in laboratory trials and we present results from a field experiment observing subjects walking through the lobby of a building. The results show that our method can closely track torso and arm movements, even with noisy and incomplete sensor data, and we present examples of social information observable from this orientation and positioning information that may be useful for social robots.     

Balancing of humanoid robot using contact force/moment control by task-oriented whole body control framework

Balancing control of humanoid robots is of great importance since it is a necessary functionality not only for maintaining a certain position without falling, but also for walking and running. For position controlled robots, the for-ce/torque sensors at each foot are utilized to measure the contact forces and moments, and these values are used to compute the joint angles to be commanded for balancing. The proposed approach in this paper is to maintain balance of torque-controlled robots by controlling contact force and moment using whole-body control framework with hierarchical structure. The control of contact force and moment is achieved by exploiting the full dynamics of the robot and the null-space motion in this control framework. This control approach enables compliant balancing behavior. In addition, in the case of double support phase, required contact force and moment are controlled using the redundancy in the contact force and moment space. These algorithms are implemented on a humanoid legged robot and the experimental results demonstrate the effectiveness of them.     

Image-based flight control of unmanned aerial vehicles (UAVs) for material handling in custom manufacturing

This paper introduces an approach for and the challenges in employing unmanned aerial vehicles (UAVs) for material handling in the emerging industrial custom manufacturing environments. Compared with conventional industrial robotic systems, UAVs offer enhanced flexibility for the design and on-the-fly variation of the pathways and workflow to optimally perform multiple tasks on demand, besides offering favorable cost and dimensional footprint factors. A fundamental challenge to the deployment of UAVs in manufacturing and other indoor industrial settings lies in ensuring the accuracy of a drone’s localization and flight path. Earlier approaches based on using multiple sensors (e.g., GPS, IMU) to improve the localization accuracy of UAVs are considered ineffective in indoor environments. In fact, few investigations have tackled the issues arising due to the limited space and complicated components and moving entities, human presence in shop-floor environments. Towards addressing this challenge, a pose estimation method that employs just a single camera onboard with a UAV, together with multiple ArUco markers positioned strategically over the shop-floor is implemented to track the real-time location of a UAV. A Kalman filter is applied to mitigate noise effects for pose estimation. To assess the performance of this method, several experiments were carried out in Texas A&M University’s manufacturing labs. The result suggests that Kalman filter can reduce the variance of pose estimation by 88.48 % compared to a conventional camera and marker-based motion tracking method (∼ 27 cm), and can localize (via averaging) the position to within 8 cm of the actual target location.     

A closed-loop approach for tracking a humanoid robot using particle filtering and depth data

Humanoid robots introduce instabilities during biped march that complicate the process of estimating their position and orientation along time. Tracking humanoid robots may be useful not only in typical applications such as navigation, but in tasks that require benchmarking the multiple processes that involve registering measures about the performance of the humanoid during walking. Small robots represent an additional challenge due to their size and mechanic limitations which may generate unstable swinging while walking. This paper presents a strategy for the active localization of a humanoid robot in environments that are monitored by external devices. The problem is faced using a particle filter method over depth images captured by an RGB-D sensor in order to effectively track the position and orientation of the robot during its march. The tracking stage is coupled with a locomotion system controlling the stepping of the robot toward a given oriented target. We present an integral communication framework between the tracking and the locomotion control of the robot based on the robot operating system, which is capable of achieving real-time locomotion tasks using a NAO humanoid robot.