scholarly journals FPGA Control of a Mobile Inverted Pendulum Robot

1970 ◽  
Vol 8 (1-2) ◽  
pp. 188-196
Author(s):  
Bikram Adhikari ◽  
Deepak Gurung ◽  
Giresh Singh Kunwar ◽  
Prashanta Gyawali
Keyword(s):  
Kalman Filter ◽  
Sensor Fusion ◽  
Pid Control ◽  
Navigation System ◽  
Inertial Navigation System ◽  
Inverted Pendulum ◽  
Inertial Sensors ◽  
Inertial Navigation ◽  
Soft Core ◽  
Quadrature Phase

The inverted pendulum is a classic problem in dynamics and control theory due to its inherently unstable nature. In the system tested, Field Programmable Gate Arrays (FPGAs) are used for the implementation of control and sensor fusion algorithms in the inertial navigation system of a Mobile Inverted Pendulum (MIP) robot. Additionally, the performance of digital PID control and Kalman filter algorithms are tested in this FPGA system. The test platform for tuning Kalman filter is designed using optical encoders as a standard reference. PWM signal generation and quadrature phase decoding of encoder pulses is accomplished using hardware description language in FPGA. The values from the inertial sensors and quadrature phase decoded values are fed into MicroBlaze, a 32-bit soft-core RISC processor, within the FPGA. The overall system demonstrates the use of low cost inertial sensors to balance a two wheeled robot. The system is presently able to balance on its own and it also serves as an extremely reconfigurable FPGA based platform to facilitate future modifications, updates and enhancements with more complex control and sensor fusion techniques.Key Terms: Mobile Inverted Pendulum System; Inverted Pendulum Robot; Inertial Navigation System; FPGA; Kalman Filter; PID Control; Soft-core ProcessorDOI: http://dx.doi.org/10.3126/jie.v8i1-2.5111Journal of the Institute of EngineeringVol. 8, No. 1&2, 2010/2011Page: 188-196Uploaded Date: 20 July, 2011

Aiding Low Cost Inertial Navigation with Building Heading for Pedestrian Navigation

2011 ◽  
Vol 64 (2) ◽  
pp. 219-233 ◽  
Author(s):  
Khairi Abdulrahim ◽  
Chris Hide ◽  
Terry Moore ◽  
Chris Hill
Keyword(s):  
Kalman Filter ◽  
Navigation System ◽  
Inertial Navigation System ◽  
Inertial Sensors ◽  
Low Cost ◽  
Inertial Navigation ◽  
Field Trials ◽  
Basic Knowledge ◽  
Effective Solution ◽  
Pedestrian Navigation

In environments where GNSS is unavailable or not useful for positioning, the use of low cost MEMS-based inertial sensors has paved a way to a more cost effective solution. Of particular interest is a foot mounted pedestrian navigation system, where zero velocity updates (ZUPT) are used with the standard strapdown navigation algorithm in a Kalman filter to restrict the error growth of the low cost inertial sensors. However heading drift still remains despite using ZUPT measurements since the heading error is unobservable. External sensors such as magnetometers are normally used to mitigate this problem, but the reliability of such an approach is questionable because of the existence of magnetic disturbances that are often very difficult to predict. Hence there is a need to eliminate the heading drift problem for such a low cost system without relying on external sensors to give a possible stand-alone low cost inertial navigation system. In this paper, a novel and effective algorithm for generating heading measurements from basic knowledge of the orientation of the building in which the pedestrian is walking is proposed to overcome this problem. The effectiveness of this approach is demonstrated through three field trials using only a forward Kalman filter that can work in real-time without any external sensors. This resulted in position accuracy better than 5 m during a 40 minutes walk, about 0·1% in position error of the total distance. Due to its simplistic algorithm, this simple yet very effective solution is appealing for a promising future autonomous low cost inertial navigation system.

Kalman Filter-Based Sensor Fusion for Improving Localization of AGV

2012 ◽  
Vol 488-489 ◽  
pp. 1818-1822 ◽  
Author(s):  
In Seong Lee ◽  
Jae Yong Kim ◽  
Jun Ha Lee ◽  
Jung Min Kim ◽  
Sung Sin Kim
Keyword(s):  
Kalman Filter ◽  
Sensor Fusion ◽  
High Precision ◽  
Navigation System ◽  
High Speed ◽  
Inertial Navigation System ◽  
Inertial Navigation ◽  
Positioning Accuracy ◽  
Global Positioning ◽  
Precision Device

This paper proposes localization using sensor fusion with a laser navigation and an inertial navigation system for indoor mobile. The laser navigation is a device that measures angle and distance between the robot and the reflectors. Although it is the high-precision device for indoor global positioning, there is a problem that the accuracy of laser navigation significantly drops while moving at high speed and rapid turning. To solve this problem, the laser navigation was fused to inertial navigation system through Kalman filter. For experiment, we use omnidirectional robot with Mecanum Wheels and analyze the positioning accuracy according to driving direction of the robot.

An Improved and Efficient Algorithm for SINS/GPS/Doppler Integrated Navigation Systems

2012 ◽  
Vol 245 ◽  
pp. 323-329 ◽  
Author(s):  
Muhammad Ushaq ◽  
Jian Cheng Fang
Keyword(s):  
Kalman Filter ◽  
Navigation System ◽  
Inertial Navigation System ◽  
Inertial Sensor ◽  
Inertial Navigation ◽  
Measurement Unit ◽  
Navigation Systems ◽  
Integrated Navigation ◽  
Efficient Manner ◽  
Position Errors

Inertial navigation systems exhibit position errors that tend to grow with time in an unbounded mode. This degradation is due, in part, to errors in the initialization of the inertial measurement unit and inertial sensor imperfections such as accelerometer biases and gyroscope drifts. Mitigation to this growth and bounding the errors is to update the inertial navigation system periodically with external position (and/or velocity, attitude) fixes. The synergistic effect is obtained through external measurements updating the inertial navigation system using Kalman filter algorithm. It is a natural requirement that the inertial data and data from the external aids be combined in an optimal and efficient manner. In this paper an efficient method for integration of Strapdown Inertia Navigation System (SINS), Global Positioning System (GPS) and Doppler radar is presented using a centralized linear Kalman filter by treating vector measurements with uncorrelated errors as scalars. Two main advantages have been obtained with this improved scheme. First is the reduced computation time as the number of arithmetic computation required for processing a vector as successive scalar measurements is significantly less than the corresponding number of operations for vector measurement processing. Second advantage is the improved numerical accuracy as avoiding matrix inversion in the implementation of covariance equations improves the robustness of the covariance computations against round off errors.

scholarly journals Application of Federal Kalman Filter with Neural Networks in the Velocity and Attitude Matching of Transfer Alignment

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-7 ◽  
Author(s):  
Lijun Song ◽  
Zhongxing Duan ◽  
Bo He ◽  
Zhe Li
Keyword(s):  
Neural Networks ◽  
Kalman Filter ◽  
Navigation System ◽  
Inertial Navigation System ◽  
Estimation Error ◽  
Inertial Navigation ◽  
Transfer Alignment ◽  
The Neural Networks ◽  
Initial Attitude ◽  
Noise Statistics

The centralized Kalman filter is always applied in the velocity and attitude matching of Transfer Alignment (TA). But the centralized Kalman has many disadvantages, such as large amount of calculation, poor real-time performance, and low reliability. In the paper, the federal Kalman filter (FKF) based on neural networks is used in the velocity and attitude matching of TA, the Kalman filter is adjusted by the neural networks in the two subfilters, the federal filter is used to fuse the information of the two subfilters, and the global suboptimal state estimation is obtained. The result of simulation shows that the federal Kalman filter based on neural networks is better in estimating the initial attitude misalignment angle of inertial navigation system (INS) when the system dynamic model and noise statistics characteristics of inertial navigation system are unclear, and the estimation error is smaller and the accuracy is higher.

Incremental predictive Kalman filter for alignment of inertial navigation system

2018 ◽  
pp. 095441001879432
Author(s):  
Mahdi Fathi ◽  
Nematollah Ghahramani ◽  
Mohammad Ali Shahi Ashtiani ◽  
Ali Mohammadi ◽  
Mohsen Fallah
Keyword(s):  
Kalman Filter ◽  
Navigation System ◽  
Inertial Navigation System ◽  
Inertial Navigation

MODELING AND NUMERICAL SIMULATION OF AN ALGORITHM FOR THE INERTIAL SENSORS ERRORS REDUCTION AND FOR THE INCREASE OF THE STRAP-DOWN NAVIGATOR REDUNDANCY DEGREE IN A LOW COST ARCHITECTURE

2010 ◽  
Vol 34 (1) ◽  
pp. 1-16 ◽  
Author(s):  
Lucian T. Grigorie ◽  
Ruxandra M. Botez
Keyword(s):  
Navigation System ◽  
Inertial Navigation System ◽  
Lower Cost ◽  
Inertial Sensors ◽  
Low Cost ◽  
Inertial Navigation ◽  
Noise Power ◽  
Integration Algorithm ◽  
The Cost ◽  
Miniaturized Sensors

In this paper, an algorithm for the inertial sensors errors reduction in a strap-down inertial navigation system, using several miniaturized inertial sensors for each axis of the vehicle frame, is conceived. The algorithm is based on the idea of the maximum ratio-combined telecommunications method. We consider that it would be much more advantageous to set a high number of miniaturized sensors on each input axis of the strap-down inertial system instead of a single one, more accurate but expensive and with larger dimensions. Moreover, a redundant system, which would isolate any of the sensors in case of its malfunctioning, is obtained. In order to test the algorithm, Simulink code is used for algorithm and for the acceleration inertial sensors modeling. The Simulink resulted sensors models include their real errors, based on the data sheets parameters, and were conceived based on the IEEE analytical standardized accelerometers model. An integration algorithm is obtained, in which the signal noise power delivered to the navigation processor, is reduced, proportionally with the number of the integrated sensors. At the same time, the bias of the resulted signal is reduced, and provides a high redundancy degree for the strap-down inertial navigation system at a lower cost than at the cost of more accurate and expensive sensors.

Analysis of the Symmetry for a Rotating Strapdown Inertial Navigation System

2012 ◽  
Vol 566 ◽  
pp. 703-706
Author(s):  
Wei Gao ◽  
Ya Zhang ◽  
Qian Sun ◽  
Yue Yang Ben
Keyword(s):  
Navigation System ◽  
Inertial Navigation System ◽  
Inertial Sensors ◽  
Inertial Navigation ◽  
Strapdown Inertial Navigation System ◽  
Basic Requirement ◽  
Position Accuracy ◽  
Rotation Method ◽  
Constant Bias ◽  
Strapdown Inertial Navigation

It is known that the precision of the strapdown inertial navigation system is influenced by constant bias of inertial sensors. A method of self-compensation based on a rotating inertial navigation system is proposed to enhance the precision. The constant drift of gyro and accelerometers is modulated into a seasonal and zero-mean form. In the paper, the theory of the rotary modulation and the basic requirement of the rotation method are analyzed. A new dual-axis rotating method is put forward. Simulations have been done. And the results indicate that the method can clear up the constant bias of the inertial sensors quickly and effectively. The position accuracy can be greatly enhanced compared with no rotary manner.

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