机器人外文文献

Keywords: forward kinematics; sensor network; sensor fusion; FPGA; industrial robot

1. Introduction

Flexible manipulator robots have wide industrial applications, with handling and manufacturing operations being some of the most common [1-3]. High-precision and high-accuracy in robot operations require the study of robot kinematics, dynamics and control [4]. The aim of forward kinematics is to compute the position and orientation of the robot end effector as a function of the angular position of each joint [1]. The online estimation of the forward kinematics can contribute to improve the controller performance by considering the joints’ motion collectively. Therefore, the precision and accuracy of such information is essential to the controller in order to increase its performance in real robotic operations.

Commercially available motion controllers use a single sensor for each joint to estimate the robot’s angular position; the most common sensor is the optical encoder [5-11], which provides a high-resolution feedback to the controller. However, it only gives information on the servomotor position and any deformations caused by joint flexibilities cannot be monitored [6,12], decreasing the robot’s accuracy. This problem is more evident in open-chain robots. Moreover, the provided information is relative, which means that it is impossible to estimate the initial position of the robot. Another sensor that is widely used in the estimation of the angular position of the robot joints is the gyroscope; it provides a measurement of angular rate of change, requiring the accumulated sum over time to estimate the angular position. Despite the fact that they can detect some nonlinearities that cannot be estimated with encoders, the quantized, noisy signal causes accumulated errors when angular position is required [13-15]. Furthermore, the estimated angular position is relative, which does not permit one to know the initial angular position of the robot joints. A good sensor that provides an absolute measurement is the accelerometer and it can be used to estimate the robot angular

position [5,16-20]. Nevertheless, the signal obtained is noisy and contains much information that needs preprocessing before being used [21].

Two main issues need to be solved when the robot forward kinematics is required: the problems of using a single sensor to estimate the angular position of the joints and the online estimation of the forward kinematics. In this perspective, sensor fusion techniques improve the accuracy of the monitored variables, but at the expense of high-computational loads [22], which complicate the online estimation of the forward kinematics. Some works combine sensor fusion techniques and forward kinematics estimation. For example, in [7] accelerometer and encoder signals are fused using a disturbance observer to compensate some nonlinearities and a reset state estimator for position estimation; experimentation is performed on a linear motor positioning system, which requires no forward kinematics estimation. Another work that fuses encoder and accelerometer signals is presented in [16], where the forward kinematics of two links in a 6-DOF robot is calculated; different versions of an extended Kalman filter are used for sensor fusion. However, the efficacy of the proposed algorithm is demonstrated offline. Other works attempt to fuse more than two different sensors. In [12] the fusion of encoder, accelerometer and interferometer through a Kalman filter is presented to estimate the position of a parallel kinematic machine, but the analysis is limited to one-axis movement. In [6] camera, accelerometer and gyroscope sensors are combined through a kinematic Kalman filter for position estimation of a planar two-link robot to facilitate the forward kinematics estimation. In [23,24] a hardware-software architecture for sensor network fusion in industrial robots is PCs to process all the data collected from the sensors and to control the robot are used. However, they use the sensor fusion to estimate the robot contact force and the forward kinematics are estimated only from the encoder signals.

Reported works note the limitations of using a single sensor to estimate robots’ forward kinematics. Besides, forward kinematics is limited to a couple of joints due to the equations’ complexity. Therefore, the online forward kinematics estimation problem for multi-axis robots still

requires additional efforts. Due to this, a dedicated processor capable of filtering and fusing the information of several sensors would be desirable. Also, multi-axis forward kinematics estimation in an online fashion would be advantageous.

The contribution of this work is performed of two stages: the improvement of the sensing method of conventional motion controllers through proposal of an encoder-accelerometer-based fused smart sensor network. Furthermore, we propose a smart processor capable of processing all the sensed encoder-accelerometer signals so as to obtain online forward kinematics estimation of each joint of a six-degree-of-freedom (6-DOF) industrial robot. The smart processor is designed using field-programmable gate arrays (FPGA) owing to their parallel computation capabilities and reconfigurability. It is designed through the combination of several methods and techniques to achieve online operation. The smart processor is able to filter and to fuse the information of the sensor network, which contains two primary sensors: an optical encoder, and a 3-axis accelerometer; and then to obtain the robot forward kinematics for each joint in an online fashion. The sensor network is composed of six encoders and four 3-axis accelerometers mounted on the robot. An experimental setup was carried out on a 6-DOF PUMA (Programmable Universal Manipulation Arm) robot, demonstrating the performance of the proposed fused smart sensor network through the monitoring of three real-operation-oriented 3D trajectories. Moreover, additional experiments were carried out whereby the forward kinematics obtained with the proposed methodology is compared against the obtained through the conventional method of using encoders. An improvement in the measurement accuracy is found when the proposed methodology is used.

2.Methodology

The use of accelerometers on 6-DOF PUMA robots requires placing them adequately in specific positions. The combination of accelerometers and encoders make up the sensor network that needs to be processed in order to estimate the angular position of each joint and the forward kinematics accurately. In this section, the placement of the sensor network on the PUMA robot is presented. Then, the FPGA-based forward kinematics smart processor is clearly described.

. Sensor Network

A sensor network is an array of diverse types of sensors to monitor several variables [25,26]; in this case the angular position of the joint flexibilities of the robot. The sensor network arranged on the robot is presented in Figure 1. Figure 1(a) depicts the position of the accelerometers on the robot. xiA, yiA and ziA are the measurements of each axis from the accelerometer iA. Figure 1(b) is a schematic of the robot including its link parameters. i represents the angular position of joint i. ia and id represents the robot physical dimensions. Also, the localization of encoders iE is shown.

Figure 1. Sensor network location on the robot.

(a) Accelerometer placement. (b) Schematic

showing the parameters that describe the robot.

For this work, the forward kinematics estimation consists on the estimation of the joint angular position (i), the spatial position of each joint (iiiZYX,,); and the roll (i), pitch (i), and yaw (i) angles [1]. Such angles represent the rotation along the000,,XYZ axes, respectively, required to obtain the orientation of each joint.

Angular Joint Position

The joint angular position is calculated with both encoder and accelerometer sensors. Afterwards, the obtained information is fused through a Kalman filter. In the case of the encoders, the angular joint position Ei can be calculated using Equation (1), where iEis the accumulated count given by encoder i; SF is a scale factor relating the minimum angular displacement required to obtain a count in the encoder, the units are rad/counts:

Concerning the estimation of the angular joint position using accelerometers Ai, the corresponding equations are summarized in Table 1. In the case of the first angular position, the joint always moves perpendicularly to gravity force. Therefore, an accelerometer cannot detect the position changes in this joint. For this reason, only the encoder information is using for the angular position estimation in joint 1. Such equations assume that the accelerometers provide a noise-free signal, which is unrealistic; thus, the signal requires a filtering stage before being used.

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人工智能专业外文翻译-机器人

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Manufacturing Engineering and Technology(机械类英文文献+翻译)

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外文翻译-多自由度步行机器人

多自由度步行机器人 摘要在现实生活中设计一款不仅可以倒下而且还可以站起来的机器人灵活智能机器人很重要。本文提出了一种两臂两足机器人,即一个模仿机器人,它可以步行、滚动和站起来。该机器人由一个头,两个胳膊和两条腿组成。基于远程控制,设计了双足机器人的控制系统,解决了机器人大脑内的机构无法与无线电联系的问题。这种远程控制使机器人具有强大的计算头脑和有多个关节轻盈的身体。该机器人能够保持平衡并长期使用跟踪视觉,通过一组垂直传感器检测是否跌倒,并通过两个手臂和两条腿履行起立动作。用实际例子对所开发的系统和实验结果进行了描述。 1 引言随着人类儿童的娱乐,对于设计的双足运动的机器人具有有站起来动作的能力是必不可少。 为了建立一个可以实现两足自动步行的机器人,设计中感知是站立还是否躺着的传感器必不可少。两足步行机器人它主要集中在动态步行,作为一种先进的控制问题来对待它。然而,在现实世界中把注意力集中在智能反应,更重要的是创想,而不是一个不会倒下的机器人,是一个倒下来可以站起来的机器人。 为了建立一个既能倒下又能站起来的机器人,机器人需要传感系统就要知道它是否跌倒或没有跌倒。虽然视觉是一个机器人最重要的遥感功能,但由于视觉系统规模和实力的限制,建立一个强大的视觉系统在机器人自己的身体上是困难的。如果我们想进一步要求动态反应和智能推理经验的基础上基于视觉的机器人行为研究,那么机器人机构要轻巧足以够迅速作出迅速反应,并有许多自由度为了显示驱动各种智能行为。至于有腿机器人,只有一个以视觉为基础的

小小的研究。面临的困难是在基于视觉有腿机器人实验研究上由硬件的显示所限制。在有限的硬件基础上是很难继续发展先进的视觉软件。为了解决这些问题和推进基于视觉的行为研究,可以通过建立远程脑的办法。身体和大脑相连的无线链路使用无线照相机和远程控制机器人,因为机体并不需要电脑板,所以它变得更加容易建立一个有许多自由度驱动的轻盈机身。 在这项研究中,我们制定了一个使用远程脑机器人的环境并且使它执行平衡的视觉和起立的手扶两足机器人,通过胳膊和腿的合作,该系统和实验结果说明如下。图 1 远程脑系统的硬件配置图 2 两组机器人的身体结构 2 远程脑系统 远程控制机器人不使用自己大脑内的机构。它留大脑在控制系统中并且与它用无线电联系。这使我们能够建立一个自由的身体和沉重大脑的机器人。身体和大脑的定义软件和硬件之间连接的接口。身体是为了适应每个研究项目和任务而设计的。这使我们提前进行研究各种真实机器人系统。 一个主要利用远程脑机器人是基于超级并行计算机上有一个大型及重型颅脑。虽然硬件技术已经先进了并拥有生产功能强大的紧凑型视觉系统的规模,但是硬件仍然很大。摄像头和视觉处理器的无线连接已经成为一种研究工具。远程脑的做法使我们在基于视觉机器人技术各种实验问题的研究上取得进展。 另一个远程脑的做法的优点是机器人机体轻巧。这开辟了与有腿移动机器人合作的可能性。至于动物,一个机器人有 4 个可以行走的四肢。我们的重点是基于视觉的适应行为的4肢机器人、机械动物,在外地进行试验还没有太多的研究。 大脑是提出的在母体环境中通过接代遗传。大脑和母体可以分享新设计