Design of Two-Wheeled Self-Balanced Robot Based on Kalman Filter

2014 ◽  
Vol 971-973 ◽  
pp. 444-449
Author(s):  
Ting Gui Li
Keyword(s):  
Kalman Filter ◽  
Data Fusion ◽  
High Efficiency ◽  
Inertial Sensor ◽  
Optimal Estimation ◽  
Sensor Data ◽  
Wheeled Robot ◽  
Kalman Filter Algorithm ◽  
Balancing Robot ◽  
Balancing Control

Using the chip MC9S12XS128, two-wheeled self-balancing robot control system is designed. Its posture information is detected by accelerometer MMA7260 and gyro NEC-03, multi inertial sensor data fusion is realized by Kalman filter, posture data optimal estimation is gotten, and the accuracy of posture sensing system is improved. Using integral separation PID control algorithm, controlling the left and right motors are accelerated or decelerated, self-balancing control of two-wheeled robot is achieved. The experimental results show that, using the hardware platform MC9S12XS128, Kalman filter algorithm has high efficiency and posture data fusion is accurate and reliable, requirements which are posture optimal estimation and inclination data real-time feedback are met, and the system is stable and can accurately and quickly realize self-balancing control of two-wheeled robot.

The Two-Wheeled Self-Balancing Mobile Robot Based on MEMS

2012 ◽  
Vol 468-471 ◽  
pp. 2678-2681
Author(s):  
Hu Sun ◽  
Yun Guo Li ◽  
Xin Biao Li ◽  
Pei Cheng
Keyword(s):  
Kalman Filter ◽  
Inertial Measurement Unit ◽  
Optimal Estimation ◽  
Measurement Unit ◽  
Robot System ◽  
Multiple Sensors ◽  
Attitude Angle ◽  
Wheeled Robot ◽  
Kalman Filter Algorithm ◽  
Sensors Fusion

In this paper, the MIMU( MEMS Inertial Measurement Unit) was used to detect the attitude angle of the two-wheeled robot. By Kalman filter, the optimal estimation of attitude angle was gotten, and which was applied to the balance controlling. In this system, FPGA is chosen as processor, and the embedded kernel was built up based on SOPC. Furthermore, the software of multiple sensors fusion has been developed. The experiment indicates that the design of this robot system is reasonable, and the Kalman filter algorithm can improve the precision of controlling effectively.

scholarly journals Development and Evaluation of a Low-Drift Inertial Sensor-Based System for Analysis of Alpine Skiing Performance

Sensors ◽  
2021 ◽  
Vol 21 (7) ◽  
pp. 2480
Author(s):  
Isidoro Ruiz-García ◽  
Ismael Navarro-Marchal ◽  
Javier Ocaña-Wilhelmi ◽  
Alberto J. Palma ◽  
Pablo J. Gómez-López ◽  
...  
Keyword(s):  
Data Fusion ◽  
Reference System ◽  
Inertial Sensor ◽  
Low Cost ◽  
Sampling Rate ◽  
Sensor Data ◽  
Fusion Algorithm ◽  
Improve Technique ◽  
Worst Case ◽  
Sensor Data Fusion

In skiing it is important to know how the skier accelerates and inclines the skis during the turn to avoid injuries and improve technique. The purpose of this pilot study with three participants was to develop and evaluate a compact, wireless, and low-cost system for detecting the inclination and acceleration of skis in the field based on inertial measurement units (IMU). To that end, a commercial IMU board was placed on each ski behind the skier boot. With the use of an attitude and heading reference system algorithm included in the sensor board, the orientation and attitude data of the skis were obtained (roll, pitch, and yaw) by IMU sensor data fusion. Results demonstrate that the proposed IMU-based system can provide reliable low-drifted data up to 11 min of continuous usage in the worst case. Inertial angle data from the IMU-based system were compared with the data collected by a video-based 3D-kinematic reference system to evaluate its operation in terms of data correlation and system performance. Correlation coefficients between 0.889 (roll) and 0.991 (yaw) were obtained. Mean biases from −1.13° (roll) to 0.44° (yaw) and 95% limits of agreements from 2.87° (yaw) to 6.27° (roll) were calculated for the 1-min trials. Although low mean biases were achieved, some limitations arose in the system precision for pitch and roll estimations that could be due to the low sampling rate allowed by the sensor data fusion algorithm and the initial zeroing of the gyroscope.

Localization and Map Building of Laser Sensor AGV Based on Kalman Filter

2013 ◽  
Vol 712-715 ◽  
pp. 1938-1943
Author(s):  
Li Xiao Guo ◽  
Fan Kun ◽  
Wen Jun Yan
Keyword(s):  
Kalman Filter ◽  
Sensor Data ◽  
Laser Sensor ◽  
Map Building ◽  
Localization Accuracy ◽  
Key Technology ◽  
Navigation Algorithm ◽  
Automatic Guided Vehicle ◽  
Kalman Filter Algorithm ◽  
Navigation Method

Localization and navigation algorithm is the key technology to determine whether or not an AGV (automatic guided vehicle) can run normally. In this paper, we summarize the popular navigation technologies first and then focus on the positioning principle of Nav200 which is adopted in our AGV system. Besides that, the map building method and the layout of the reflective board is also introduced briefly. This paper introduced two navigation methods. The traditional navigation method only uses the sensor data and the electronic map to guide AGV. To improve positioning accuracy, we use the Kalman filter to minimize the error of localization sensor. At last some simulation work was done, the results shows that the localization accuracy was improved by adopting Kalman filter algorithm.

scholarly journals A Rapid Transfer Alignment Method for SINS Based on the Added Backward-Forward SINS Resolution and Data Fusion

2013 ◽  
Vol 2013 ◽  
pp. 1-10 ◽  
Author(s):  
Xixiang Liu ◽  
Xiaosu Xu ◽  
Yiting Liu ◽  
Lihui Wang
Keyword(s):  
Data Fusion ◽  
Reference Data ◽  
Inertial Sensor ◽  
Sensor Data ◽  
Alignment Method ◽  
Initial Alignment ◽  
Transfer Alignment ◽  
Gyro Bias ◽  
Sensor Errors ◽  
Rapid Transfer

Two viewpoints are given: (1) initial alignment of strapdown inertial navigation system (SINS) can be fulfilled with a set of inertial sensor data; (2) estimation time for sensor errors can be shortened by repeated data fusion on the added backward-forward SINS resolution results and the external reference data. Based on the above viewpoints, aiming to estimate gyro bias in a shortened time, a rapid transfer alignment method, without any changes for Kalman filter, is introduced. In this method, inertial sensor data and reference data in one reference data update cycle are stored, and one backward and one forward SINS resolutions are executed. Meanwhile, data fusion is executed when the corresponding resolution ends. With the added backward-forward SINS resolution, in the above mentioned update cycle, the estimating operations for gyro bias are added twice, and the estimation time for it is shortened. In the ship swinging condition, with the “velocity plus yaw” matching, the effectiveness of this method is proved by the simulation.

Weighted Multi-Sensor Data Fusion Based on Fuzzy Kalman Filter for Seam Tracking of the Welding Robots

2012 ◽  
Vol 542-543 ◽  
pp. 800-805 ◽  
Author(s):  
Jun Du ◽  
Mei Sun ◽  
Liang Hua ◽  
Jia Sheng Ge ◽  
Ju Ping Gu
Keyword(s):  
Kalman Filter ◽  
Data Fusion ◽  
Maximum Modulus ◽  
Seam Tracking ◽  
Measurement Noise ◽  
Sensor Data ◽  
Inference System ◽  
Sensor Data Fusion ◽  
Noise Characteristics ◽  
Multi Sensor Data Fusion

In order to resolve the problem of seam tracking of the welding robots with unknown noise characteristics, a Weighted Multi-Sensor Data Fusion (MSDF) algorithm based on the fuzzy Kalman filter algorithm is proposed. Firstly, each Fuzzy Kalman Filter (FKF) uses a fuzzy inference system based on a covariance matching technique to adjust the weight coefficient of measurement noise covariance matrix, so it makes measurement noise close to the true noise level. Secondly, a membership function in fuzzy set is used to measure the mutual support degree matrix of each FKF and corresponding weight coefficients are allocated by this matrix’s maximum modulus eigenvectors, hence, the final expression of data fusion is obtained. Finally, simulation results show that MSDF in seam tracking has both high precision and strong ability of stableness.

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