CN102121828A - Method for estimating body posture angle of humanoid robot in real time - Google Patents
Method for estimating body posture angle of humanoid robot in real time Download PDFInfo
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Abstract
本发明公开了一种仿人机器人身体姿态角度实时估计方法，该方法首先建立机器人姿态的运动学模型；然后根据机器人姿态的运动学模型，建立带噪声的线性离散随机系统模型，利用卡尔曼滤波的相应方法，基于三轴加速度计的加速度数据，三轴陀螺仪的角速度数据和六维力/力矩传感器三维力数据估计机器人的姿态状态，再根据估计的机器人姿态信息，得到机器人实际的姿态角度，从而矫正机器人当前的姿态；本方法克服了角速度积分的漂移以及传统线性化方法测量范围较小的缺陷，实现了仿人机器人姿态实时准确的估计。
The invention discloses a method for estimating the body posture angle of a humanoid robot in real time. The method firstly establishes a kinematics model of the robot posture; The corresponding method is to estimate the attitude state of the robot based on the acceleration data of the three-axis accelerometer, the angular velocity data of the three-axis gyroscope and the three-dimensional force data of the six-dimensional force/torque sensor, and then obtain the actual attitude angle of the robot according to the estimated robot attitude information , so as to correct the current attitude of the robot; this method overcomes the drift of the angular velocity integral and the defect of the small measurement range of the traditional linearization method, and realizes the real-time and accurate estimation of the attitude of the humanoid robot.
Description
技术领域technical field
本发明属于机器人领域，尤其涉及一种基于六维力/力矩传感器，加速度计和陀螺仪的仿人机器人姿态估计方法。The invention belongs to the field of robots, and in particular relates to a posture estimation method of a humanoid robot based on a six-dimensional force/torque sensor, an accelerometer and a gyroscope.
技术背景technical background
仿人机器人是一种模仿人体骨骼结构的机器人，它与其他机器人的最大区别在于其仿人的双足运动方式。仿生分析表明，仿人机器人具有环境适应能力强、动作灵活等优点，因此，具有更加广阔的应用和发展前景。A humanoid robot is a robot that imitates the skeleton structure of the human body. The biggest difference between it and other robots lies in its humanoid bipedal movement. Bionic analysis shows that humanoid robots have the advantages of strong environmental adaptability and flexible movements, so they have broader application and development prospects.
与轮式机器人和工业机器人不同，仿人机器人的支撑域更小，更容易摔倒。因此需要知道机器人的姿态角度，包括俯仰和滚动角度，以便检测机器人的状态，预防机器人摔倒。Unlike wheeled and industrial robots, humanoid robots have a smaller field of support and are more prone to falls. Therefore, it is necessary to know the attitude angle of the robot, including pitch and roll angles, in order to detect the state of the robot and prevent the robot from falling.
一种比较常用的姿态估计算法是先对角速度的积分经过一个高通滤波器得到角度的高频信号，再对经加速度计算得到的角度经过一个低通滤波器，得到姿态角度的低频信号，这两个角度信号的叠加可以得到完整的姿态角度(参见“K. Loffler, M.Gienger, F.Pfeiffer：Sensor and Control Design of a Dynamically Stable Biped Robot，International Conference on Robotics and Automation，September，2003”) ，但是该方法需要设定合适的低通滤波器和高通滤波器的参数，以达到准确的估计，而且对于不同的步行模式，需要选择不同的滤波器参数，因此该方法的适用范围较小。A more commonly used attitude estimation algorithm is to first pass the integral of angular velocity through a high-pass filter to obtain the high-frequency signal of the angle, and then pass a low-pass filter to the angle obtained by the acceleration calculation to obtain the low-frequency signal of the attitude angle. The complete attitude angle can be obtained by superposition of two angle signals (see "K. Loffler, M. Gienger, F. Pfeiffer: Sensor and Control Design of a Dynamically Stable Biped Robot, International Conference on Robotics and Automation, September, 2003"), However, this method needs to set appropriate low-pass filter and high-pass filter parameters to achieve accurate estimation, and for different walking patterns, different filter parameters need to be selected, so the scope of application of this method is small.
由于姿态估计的非线性，另外一种常见的方法是，对姿态运动学模型在平衡点进行线性化处理，得到该运动学方程的线性模型，利用kalman滤波的方法估计该线性模型的状态，从而得到姿态角度（参见“ An Attitude Control System for small unmanned helicopters based on gyrometer and angular rate gyroscDue to the nonlinearity of attitude estimation, another common method is to linearize the attitude kinematics model at the equilibrium point to obtain the linear model of the kinematic equation, and use the kalman filter method to estimate the state of the linear model, so that Get the attitude angle (see "An Attitude Control System for small unmanned helicopters based on gyrometer and angular rate gyrosc
Henrik Rehbinder和Xiaoming Hu 提出了姿态估计的另一种方法（参见“Drift-free attitude estimation for accelerated rigid bodies，Automatica, 2004”）。在该方法中，利用重力加速度修正机器人的姿态角度。但是该方法需要机器人的横向加速度较小。对于仿人机器人步行中的加速度变化，由于横向加速度引入的模型误差并不能忽略，所以该方法并不适用于仿人机器人的步行。Another approach to pose estimation was proposed by Henrik Rehbinder and Xiaoming Hu (see "Drift-free attitude estimation for accelerated rigid bodies, Automatica, 2004"). In this method, the attitude angle of the robot is corrected using the acceleration of gravity. But this method requires less lateral acceleration of the robot. For the acceleration variation in humanoid robot walking, the model error introduced by lateral acceleration cannot be ignored, so this method is not suitable for humanoid robot walking.
中国发明专利申请200810224579.4提出了一个仿人机器人足部姿态实时检测系统及方法，该姿态检测方法利用三轴加速度信息计算得到静态倾角信息；对采集的角速度信息进行计算得到角度的变化值；根据所述的静态面倾角信息和所述的角度变化值，得到实时的倾角信息。但是由于机器人运动过程中与地面的碰撞，利用加速度计计算所得的静态倾角信息并不准确，而且该方案也没办法克服角速度的积分误差。Chinese invention patent application 200810224579.4 proposes a real-time detection system and method for the foot posture of a humanoid robot. The posture detection method uses the three-axis acceleration information to calculate the static inclination information; calculates the collected angular velocity information to obtain the change value of the angle; according to the obtained The above-mentioned static surface inclination information and the above-mentioned angle change value are used to obtain real-time inclination information. However, due to the collision between the robot and the ground during the movement, the static inclination information calculated by the accelerometer is not accurate, and this scheme cannot overcome the integral error of the angular velocity.
因此，有必要设计一种能够实时，快速并且能有效克服陀螺仪积分误差的仿人机器人姿态角度估计方案。Therefore, it is necessary to design a humanoid robot attitude angle estimation scheme that can be real-time, fast and effectively overcome the integral error of the gyroscope.
发明内容Contents of the invention
本发明的目的是针对现有技术的不足，提供了一种仿人机器人身体姿态角度实时估计方法。The purpose of the present invention is to provide a real-time estimation method for the body attitude angle of a humanoid robot aiming at the deficiencies in the prior art.
本发明的目的是通过以下技术方案来实现的：一种仿人机器人身体姿态角度实时估计方法，仿人机器人姿态估计系统包括三轴陀螺仪、三轴加速度计、kalman滤波器和两个六维力/力矩传感器，其中，三轴陀螺仪、三轴加速度计、和两个六维力/力矩传感器分别与kalman滤波器相连；该方法包括以下几个步骤：The object of the present invention is achieved through the following technical solutions: a real-time estimation method for the body attitude angle of a humanoid robot, the attitude estimation system of the humanoid robot includes a three-axis gyroscope, a three-axis accelerometer, a kalman filter and two six-dimensional A force/torque sensor, wherein a three-axis gyroscope, a three-axis accelerometer, and two six-dimensional force/torque sensors are respectively connected with a kalman filter; the method comprises the following steps:
（1）安装六维力/力矩传感器及三轴陀螺仪和三轴加速度计；其中，两个六维力/力矩传感器分别安装在机器人的两个踝关节上，三轴加速度计和三轴陀螺仪安装在机器人的髋部中心点。(1) Install six-dimensional force/torque sensors, three-axis gyroscopes and three-axis accelerometers; among them, two six-dimensional force/torque sensors are installed on the two ankle joints of the robot, three-axis accelerometers and three-axis gyroscopes The instrument is installed at the hip center point of the robot.
（2）建立机器人姿态的运动学模型；(2) Establish the kinematics model of the robot attitude;
（3）根据机器人姿态的运动学模型，建立带噪声的线性离散随机系统模型，利用卡尔曼滤波的相应方法，基于三轴加速度计的加速度数据，三轴陀螺仪的角速度数据和六维力/力矩传感器三维力数据估计机器人的姿态状态；(3) According to the kinematics model of the robot attitude, a linear discrete random system model with noise is established, using the corresponding method of Kalman filter, based on the acceleration data of the three-axis accelerometer, the angular velocity data of the three-axis gyroscope and the six-dimensional force/ The three-dimensional force data of the torque sensor estimates the attitude state of the robot;
（4）根据估计的机器人姿态信息，得到机器人实际的姿态角度；(4) Obtain the actual attitude angle of the robot according to the estimated attitude information of the robot;
（5）根据得到的机器人实际的姿态角度，矫正机器人当前的姿态。 (5) Correct the current posture of the robot according to the obtained actual posture angle of the robot .
本发明的有益效果是，本发明仿人机器人身体姿态角度实时估计方法通过软测量的技术实现，根据机器人姿态的运动学方程，利用kalman滤波的方法，得到机器人姿态状态的最优估计，从而计算出相应的俯仰和滚动角度。本方法克服了角速度积分的漂移以及传统线性化方法测量范围较小的缺陷，实现了仿人机器人姿态实时准确的估计。The beneficial effect of the present invention is that the real-time estimation method of the body attitude angle of the humanoid robot of the present invention is realized through the technology of soft sensing, and according to the kinematic equation of the robot attitude, the optimal estimation of the attitude state of the robot is obtained by using the kalman filter method, thereby calculating corresponding pitch and roll angles. This method overcomes the drift of the angular velocity integral and the defect of the small measurement range of the traditional linearization method, and realizes the real-time and accurate estimation of the attitude of the humanoid robot.
附图说明Description of drawings
图1是仿人机器人姿态估计系统的整体框图；Figure 1 is an overall block diagram of a humanoid robot pose estimation system;
图2是传感器的安装示意图；Figure 2 is a schematic diagram of the installation of the sensor;
图3是机器人稳定行走时身体姿态的滚动角度数据图；Fig. 3 is a data diagram of the rolling angle of the body posture when the robot walks stably;
图4是机器人稳定行走时身体姿态的俯仰角度数据图。Fig. 4 is a data diagram of the pitch angle of the body posture when the robot walks stably.
具体实施方式Detailed ways
Kalman滤波的原理如下：利用给定的系统输出观测序列 
如图1所示，仿人机器人姿态估计系统包括三轴陀螺仪、三轴加速度计、kalman滤波器和两个六维力/力矩传感器，其中，三轴陀螺仪、三轴加速度计、和两个六维力/力矩传感器分别与kalman滤波器相连。三轴加速度计提供机器人运动时的三轴线性加速度数据，三轴陀螺仪提供三轴的角速度数据，两个六维力/力矩传感器提供三维力数据。基于机器人姿态变化的运动学模型建立系统状态方程，根据传感器获得的力数据，加速度数据以及角速度数据，通过kalman滤波的方法得到机器人姿态的最优估计。根据最优估计得到机器人姿态，计算可得相应的姿态角度。As shown in Figure 1, the humanoid robot attitude estimation system includes a three-axis gyroscope, a three-axis accelerometer, a kalman filter and two six-dimensional force/torque sensors, among which, the three-axis gyroscope, three-axis accelerometer, and two Each six-dimensional force/torque sensor is connected with kalman filter respectively. The three-axis accelerometer provides three-axis linear acceleration data when the robot is moving, the three-axis gyroscope provides three-axis angular velocity data, and two six-dimensional force/torque sensors provide three-dimensional force data. The system state equation is established based on the kinematics model of the robot's attitude change, and the optimal estimation of the robot's attitude is obtained through the kalman filter method according to the force data, acceleration data and angular velocity data obtained by the sensor. According to the optimal estimation, the robot attitude is obtained, and the corresponding attitude angle can be obtained by calculation.
本发明仿人机器人身体姿态角度实时估计方法，包括以下几个步骤：首先，安装六维力/力矩传感器及三轴陀螺仪和加速度计传感器；其次，建立机器人姿态的运动学模型；再次，根据机器人姿态的运动学模型，建立带噪声的线性离散随机系统模型，利用卡尔曼滤波的相应方法，基于三轴加速度计的加速度数据，三轴陀螺仪的角速度数据和六维力/力矩传感器三维力数据估计机器人的姿态状态；根据估计后的机器人姿态信息，得到机器人实际的姿态角度。最后，根据得到的机器人姿态俯仰和滚动角度，矫正机器人当前的姿态。The method for estimating the body attitude angle of a humanoid robot in real time in the present invention comprises the following steps: first, install a six-dimensional force/moment sensor and a three-axis gyroscope and an accelerometer sensor; secondly, establish a kinematics model of the robot attitude; again, according to Kinematics model of robot attitude, establish a linear discrete random system model with noise, use the corresponding method of Kalman filter, based on the acceleration data of the three-axis accelerometer, the angular velocity data of the three-axis gyroscope and the three-dimensional force of the six-dimensional force/torque sensor The attitude state of the robot is estimated from the data; according to the estimated attitude information of the robot, the actual attitude angle of the robot is obtained. Finally, correct the current attitude of the robot according to the obtained robot attitude pitch and roll angles.
具体如下，details as follows,
第一步，安装六维力/力矩传感器及三轴陀螺仪和加速度计传感器The first step is to install the six-dimensional force/torque sensor and the three-axis gyroscope and accelerometer sensor
传感器的安装示意图如图2所示：两个六维力/力矩传感器分别安装在机器人的两个踝关节（ 脚板与小腿的连接部位）上，并且传感器局部坐标系的x轴正方向沿机器人前进方向，y轴正方向从右至左，z轴正方向竖直向上。三轴加速度计和三轴陀螺仪安装在机器人的髋部中心点，并且其局部坐标系方向和六维力/力矩传感器的坐标系方向一致。The installation diagram of the sensor is shown in Figure 2: two six-dimensional force/torque sensors are respectively installed on the two ankle joints of the robot (the connection between the foot plate and the lower leg), and the positive direction of the x-axis of the local coordinate system of the sensor moves forward along the robot Direction, the positive direction of the y-axis is from right to left, and the positive direction of the z-axis is vertically upward. The three-axis accelerometer and the three-axis gyroscope are installed at the hip center point of the robot, and the direction of its local coordinate system is consistent with the direction of the coordinate system of the six-dimensional force/torque sensor.
第二步，建立机器人姿态的运动学模型。The second step is to establish the kinematics model of the robot pose.
姿态可以用3×3的旋转矩阵来描述，对应绕x轴的旋转角称为滚动角（），相应的旋转矩阵：The attitude can be described by a 3×3 rotation matrix, and the corresponding rotation angle around the x-axis is called the roll angle ( ), the corresponding rotation matrix:
对应绕y轴的旋转角度称为俯仰角（
对应绕z轴的旋转角度称为偏转角()，相应的旋转矩阵为：The angle of rotation corresponding to the z-axis is called the deflection angle ( ), the corresponding rotation matrix is:
对于机器人的姿态，我们用欧拉角度按偏转（
对该旋转矩阵求导：Take the derivative of this rotation matrix:
把写为两个矩阵的组合，
对上式进行角度微分变换得，The angle differential transformation of the above formula is obtained,
所以so
安装在机器人髋部中心点的三轴加速度计和三轴陀螺仪，能够实时测量机器人髋部中心点沿自身坐标系的加速度和相应的角速度。当机器人静止的时候，加速度计测量的加速度为重力加速度。当机器人运动的时候，地面的摩擦力提供机器人前进的动力，机器人受到的加速度由合外力提供（见图2）。The three-axis accelerometer and the three-axis gyroscope installed at the center of the robot's hip can measure the acceleration and corresponding angular velocity of the center of the robot's hip along its own coordinate system in real time. When the robot is stationary, the acceleration measured by the accelerometer is the acceleration due to gravity. When the robot moves, the frictional force of the ground provides the driving force for the robot to move forward, and the acceleration received by the robot is provided by the resultant external force (see Figure 2).
假设机器人的脚板与地面完全接触，并且机器人是刚性的。我们以地面为世界坐标系的原点，建立笛卡尔右手坐标系，x轴的正方向为机器人的前进方向，z轴的正方先垂直地面向上。并且机器人髋部的加速度计和姿态传感器的初始姿态与世界坐标系的姿态相同。机器人足部受到的合外力f，在机器人髋部的加速度计上引起的加速度为a，此时机器人的姿态为R，则：Assume that the robot's feet are in full contact with the ground and that the robot is rigid. We take the ground as the origin of the world coordinate system and establish a Cartesian right-handed coordinate system. The positive direction of the x-axis is the forward direction of the robot, and the positive direction of the z-axis is first vertical to the ground. And the initial pose of the accelerometer and attitude sensor of the robot hip is the same as that of the world coordinate system. The resultant external force f received by the robot’s foot causes the acceleration on the accelerometer of the robot’s hip to be a, and the posture of the robot is R at this time, then:
a = R*(f/M)a = R*(f/M)
考虑到姿态R的状态转移方程，则机器人姿态的运动学模型为：Considering the state transition equation of attitude R, the kinematics model of robot attitude is:
其中，M为机器人的质量，
第三步，根据机器人姿态的运动学模型，建立带噪声的线性离散随机系统模型，The third step is to establish a linear discrete random system model with noise based on the kinematics model of the robot attitude, 再利用卡尔曼滤波的相应方法，基于三轴加速度计的加速度数据，三轴陀螺仪的角速度数据和六维力/力矩传感器三维力数据估计机器人的姿态状态。Then use the corresponding method of Kalman filter to estimate the attitude state of the robot based on the acceleration data of the three-axis accelerometer, the angular velocity data of the three-axis gyroscope and the three-dimensional force data of the six-dimensional force/torque sensor.
考虑到R为3×3的矩阵，测量的加速度a和合外力f为3×1向量，对姿态矩阵按行拉直，同时假设传感器测量的噪声白噪声，则系统模型可重构为：Considering that R is a 3×3 matrix, the measured acceleration a and the resultant external force f are 3×1 vectors, the attitude matrix is straightened by rows, and assuming that the noise measured by the sensor is white noise, the system model can be reconstructed as:
式中状态变量
对该模型状态的估计采用离散化的Kalman滤波方程，则以Ts为离散时间间隔，该模型的离散化方程为：The discretized Kalman filter equation is used to estimate the state of the model, and Ts is used as the discrete time interval. The discretized equation of the model is:
其中，
以状态的提前一步预报量
Kalman预报增益矩阵为：Kalman forecast gain matrix for:
第四步，根据估计后的机器人姿态信息，得到机器人实际的姿态角度。The fourth step is to obtain the actual attitude angle of the robot according to the estimated attitude information of the robot.
利用kalman滤波算法，在线估算
式中
第五步，根据得到的机器人姿态俯仰和滚动角度，矫正机器人当前的姿态。The fifth step is to correct the current attitude of the robot according to the obtained robot attitude pitch and roll angles.
为了保持机器人的稳定运行，机器人运行过程的设定的滚动和俯仰角度为0，通过控制机器人髋部关节的俯仰和滚动的关节角度，利用PID调整方式，实现机器人身体姿态的稳定控制。In order to maintain the stable operation of the robot, the rolling and pitching angles of the robot are set to 0 during the running process. By controlling the pitching and rolling joint angles of the robot's hip joints, the stable control of the robot's body posture is realized by using the PID adjustment method.
实施例1Example 1
图3和图4示出了在仿人机器人上应用本发明的方法和现有积分方法的的不同之处，设定机器人的步行速度为0.8km/h，由图3可以看出，由于机器人髋部刚性的不足，机器人在行走过程中，滚动角度存在周期性变化，该数据反映了机器人运动过程中的滚动角度变化。由于机器人摆放的初始位置或者零位的偏差，导致滚动角度并不为零。从与积分方法的对比可以看出，该方法有效地克服了角速度积分的漂移问题。由图4可以看出，当机器人运动结束后，俯仰角度的数据始终保持稳定。综上所述，该方法能有效克服角速度积分的漂移问题，反映机器人运动过程中俯仰和滚动姿态角度的变化，实现机器人俯仰和滚动姿态角度的有效测量。Fig. 3 and Fig. 4 show the difference of applying the method of the present invention and the existing integration method on the humanoid robot, the walking speed of setting robot is 0.8km/h, as can be seen from Fig. 3, because robot The rigidity of the hip is insufficient, and the rolling angle of the robot changes periodically during walking, and this data reflects the change of the rolling angle during the movement of the robot. Due to the deviation of the initial position or zero position of the robot, the roll angle is not zero. From the comparison with the integral method, it can be seen that this method effectively overcomes the drift problem of angular velocity integration. It can be seen from Figure 4 that when the robot motion is over, the data of the pitch angle remains stable all the time. In summary, this method can effectively overcome the drift problem of the angular velocity integral, reflect the changes in the pitch and roll attitude angles of the robot during motion, and realize the effective measurement of the robot pitch and roll attitude angles.
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|---|---|---|---|---|
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Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JPH09273938A (en) * | 1996-04-04 | 1997-10-21 | Takao Yamaguchi | Hybrid inertial navigation apparatus |
| WO1999020980A1 (en) * | 1997-10-20 | 1999-04-29 | Churyo Engineering Co., Ltd. | Position measurement method for mobile station |
| CN101100059A (en) * | 2007-07-27 | 2008-01-09 | 北京工业大学 | Attitude detection method of flexible two-wheeled self-balancing robot |
| CN101403619A (en) * | 2008-10-21 | 2009-04-08 | 北京理工大学 | Foot gesture real-time detection system and method for human-imitated robot |
| CN101672650A (en) * | 2009-09-29 | 2010-03-17 | 北京航空航天大学 | Orienting and locating navigation system in circumstance of electromagnetic interference |
-
2010
- 2010-12-21 CN CN 201010597601 patent/CN102121828B/en not_active Expired - Fee Related
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JPH09273938A (en) * | 1996-04-04 | 1997-10-21 | Takao Yamaguchi | Hybrid inertial navigation apparatus |
| WO1999020980A1 (en) * | 1997-10-20 | 1999-04-29 | Churyo Engineering Co., Ltd. | Position measurement method for mobile station |
| CN101100059A (en) * | 2007-07-27 | 2008-01-09 | 北京工业大学 | Attitude detection method of flexible two-wheeled self-balancing robot |
| CN101403619A (en) * | 2008-10-21 | 2009-04-08 | 北京理工大学 | Foot gesture real-time detection system and method for human-imitated robot |
| CN101672650A (en) * | 2009-09-29 | 2010-03-17 | 北京航空航天大学 | Orienting and locating navigation system in circumstance of electromagnetic interference |
Cited By (26)
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