4,061 research outputs found
Human Motion Capture Algorithm Based on Inertial Sensors
On the basis of inertial navigation, we conducted a comprehensive analysis of the human body kinematics principle. From the direction of two characteristic parameters, namely, displacement and movement angle, we calculated the attitude of a node during the human motion capture process by combining complementary and Kalman filters. Then, we evaluated the performance of the proposed attitude strategy by selecting different platforms as the validation object. Results show that the proposed strategy for the real-time tracking of the human motion process has higher accuracy than the traditional strategy
Disturbance Observer-based Robust Control and Its Applications: 35th Anniversary Overview
Disturbance Observer has been one of the most widely used robust control
tools since it was proposed in 1983. This paper introduces the origins of
Disturbance Observer and presents a survey of the major results on Disturbance
Observer-based robust control in the last thirty-five years. Furthermore, it
explains the analysis and synthesis techniques of Disturbance Observer-based
robust control for linear and nonlinear systems by using a unified framework.
In the last section, this paper presents concluding remarks on Disturbance
Observer-based robust control and its engineering applications.Comment: 12 pages, 4 figure
Motion-based remote control device for interaction with multimedia content
This dissertation describes the development and implementation of techniques to enhance
the accuracy of low-complexity lters, making them suitable for remote control devices
in consumer electronics. The evolution veri ed in the last years, on multimedia contents,
available for consumers in Smart TVs and set-top-boxes, is not raising the expected
interest from users, and one of the pointed reasons for this nding is the user interface.
Although most current pointing devices rely on relative rotation increments, absolute
orientation allows for a more intuitive use and interaction. This possibility is explored in
this work as well as the interaction with multimedia contents through gestures.
Classical accurate fusion algorithms are computationally intensive, therefore their implementation
in low-energy consumption devices is a challenging task. To tackle this
problem, a performance study was carried, comparing a relevant set of professional commercial
of-the-shelf units, with the developed low-complexity lters in state-of-the-art
Magnetic, Angular Rate, Gravity (MARG) sensors. Part of the performance evaluation
tests are carried out under harsh conditions to observe the algorithms response in a nontrivial
environment. The results demonstrate that the implementation of low-complexity
lters using low-cost sensors, can provide an acceptable accuracy in comparison with the
more complex units/ lters. These results pave the way for faster adoption of absolute
orientation-based pointing devices in interactive multimedia applications, which includes
hand-held, battery-operated devices
Advances in Spacecraft Systems and Orbit Determination
"Advances in Spacecraft Systems and Orbit Determinations", discusses the development of new technologies and the limitations of the present technology, used for interplanetary missions. Various experts have contributed to develop the bridge between present limitations and technology growth to overcome the limitations. Key features of this book inform us about the orbit determination techniques based on a smooth research based on astrophysics. The book also provides a detailed overview on Spacecraft Systems including reliability of low-cost AOCS, sliding mode controlling and a new view on attitude controller design based on sliding mode, with thrusters. It also provides a technological roadmap for HVAC optimization. The book also gives an excellent overview of resolving the difficulties for interplanetary missions with the comparison of present technologies and new advancements. Overall, this will be very much interesting book to explore the roadmap of technological growth in spacecraft systems
์ ๋ถ ๋ฐ ๋งค๊ฐ๋ณ์ ๊ธฐ๋ฒ ์ตํฉ์ ์ด์ฉํ ์ค๋งํธํฐ ๋ค์ค ๋์์์ ๋ณดํ ํญ๋ฒ
ํ์๋
ผ๋ฌธ (๋ฐ์ฌ) -- ์์ธ๋ํ๊ต ๋ํ์ : ๊ณต๊ณผ๋ํ ๊ธฐ๊ณํญ๊ณต๊ณตํ๋ถ, 2020. 8. ๋ฐ์ฐฌ๊ตญ.In this dissertation, an IA-PA fusion-based PDR (Pedestrian Dead Reckoning) using low-cost inertial sensors is proposed to improve the indoor position estimation. Specifically, an IA (Integration Approach)-based PDR algorithm combined with measurements from PA (Parametric Approach) is constructed so that the algorithm is operated even in various poses that occur when a pedestrian moves with a smartphone indoors. In addition, I propose an algorithm that estimates the device attitude robustly in a disturbing situation by an ellipsoidal method. In addition, by using the machine learning-based pose recognition, it is possible to improve the position estimation performance by varying the measurement update according to the poses.
First, I propose an adaptive attitude estimation based on ellipsoid technique to accurately estimate the direction of movement of a smartphone device. The AHRS (Attitude and Heading Reference System) uses an accelerometer and a magnetometer as measurements to calculate the attitude based on the gyro and to compensate for drift caused by gyro sensor errors. In general, the attitude estimation performance is poor in acceleration and geomagnetic disturbance situations, but in order to effectively improve the estimation performance, this dissertation proposes an ellipsoid-based adaptive attitude estimation technique. When a measurement disturbance comes in, it is possible to update the measurement more accurately than the adaptive estimation technique without considering the direction by adjusting the measurement covariance with the ellipsoid method considering the direction of the disturbance. In particular, when the disturbance only comes in one axis, the proposed algorithm can use the measurement partly by updating the other two axes considering the direction. The proposed algorithm shows its effectiveness in attitude estimation under disturbances through the rate table and motion capture equipment.
Next, I propose a PDR algorithm that integrates IA and PA that can be operated in various poses. When moving indoors using a smartphone, there are many degrees of freedom, so various poses such as making a phone call, texting, and putting a pants pocket are possible. In the existing smartphone-based positioning algorithms, the position is estimated based on the PA, which can be used only when the pedestrian's walking direction and the device's direction coincide, and if it does not, the position error due to the mismatch in angle is large. In order to solve this problem, this dissertation proposes an algorithm that constructs state variables based on the IA and uses the position vector from the PA as a measurement. If the walking direction and the device heading do not match based on the pose recognized through machine learning technique, the position is updated in consideration of the direction calculated using PCA (Principal Component Analysis) and the step length obtained through the PA. It can be operated robustly even in various poses that occur.
Through experiments considering various operating conditions and paths, it is confirmed that the proposed method stably estimates the position and improves performance even in various indoor environments.๋ณธ ๋
ผ๋ฌธ์์๋ ์ ๊ฐํ ๊ด์ฑ์ผ์๋ฅผ ์ด์ฉํ ๋ณดํํญ๋ฒ์์คํ
(PDR: Pedestrian Dead Reckoning)์ ์ฑ๋ฅ ํฅ์ ์๊ณ ๋ฆฌ์ฆ์ ์ ์ํ๋ค. ๊ตฌ์ฒด์ ์ผ๋ก ๋ณดํ์๊ฐ ์ค๋ด์์ ์ค๋งํธํฐ์ ๋ค๊ณ ์ด๋ํ ๋ ๋ฐ์ํ๋ ๋ค์ํ ๋์ ์ํฉ์์๋ ์ด์ฉ๋ ์ ์๋๋ก, ๋งค๊ฐ๋ณ์ ๊ธฐ๋ฐ ์ธก์ ์น๋ฅผ ์ฌ์ฉํ๋ ์ ๋ถ ๊ธฐ๋ฐ์ ๋ณดํ์ ํญ๋ฒ ์๊ณ ๋ฆฌ์ฆ์ ๊ตฌ์ฑํ๋ค. ๋ํ ํ์์ฒด ๊ธฐ๋ฐ ์์ธ ์ถ์ ์๊ณ ๋ฆฌ์ฆ์ ๊ตฌ์ฑํ์ฌ ์ธ๋ ์ํฉ์์๋ ๊ฐ์ธํ๊ฒ ์์ธ๋ฅผ ์ถ์ ํ๋ ์๊ณ ๋ฆฌ์ฆ์ ์ ์ํ๋ค. ์ถ๊ฐ์ ์ผ๋ก ๊ธฐ๊ณํ์ต ๊ธฐ๋ฐ์ ๋์ ์ธ์ ์ ๋ณด๋ฅผ ์ด์ฉ, ๋์์ ๋ฐ๋ฅธ ์ธก์ ์น ์
๋ฐ์ดํธ๋ฅผ ๋ฌ๋ฆฌํจ์ผ๋ก์จ ์์น ์ถ์ ์ฑ๋ฅ์ ํฅ์์ํจ๋ค.
๋จผ์ ์ค๋งํธํฐ ๊ธฐ๊ธฐ์ ์ด๋ ๋ฐฉํฅ์ ์ ํํ๊ฒ ์ถ์ ํ๊ธฐ ์ํด ํ์์ฒด ๊ธฐ๋ฒ ๊ธฐ๋ฐ ์ ์ ์์ธ ์ถ์ ์ ์ ์ํ๋ค. ์์ธ ์ถ์ ๊ธฐ๋ฒ (AHRS: Attitude and Heading Reference System)์ ์์ด๋ก๋ฅผ ๊ธฐ๋ฐ์ผ๋ก ์์ธ๋ฅผ ๊ณ์ฐํ๊ณ ์์ด๋ก ์ผ์์ค์ฐจ์ ์ํด ๋ฐ์ํ๋ ๋๋ฆฌํํธ๋ฅผ ๋ณด์ ํ๊ธฐ ์ํด ์ธก์ ์น๋ก ๊ฐ์๋๊ณ์ ์ง์๊ณ๋ฅผ ์ฌ์ฉํ๋ค. ์ผ๋ฐ์ ์ผ๋ก ๊ฐ์ ๋ฐ ์ง์๊ณ ์ธ๋ ์ํฉ์์๋ ์์ธ ์ถ์ ์ฑ๋ฅ์ด ๋จ์ด์ง๋๋ฐ, ์ถ์ ์ฑ๋ฅ์ ํจ๊ณผ์ ์ผ๋ก ํฅ์์ํค๊ธฐ ์ํด ๋ณธ ๋
ผ๋ฌธ์์๋ ํ์์ฒด ๊ธฐ๋ฐ ์ ์ ์์ธ ์ถ์ ๊ธฐ๋ฒ์ ์ ์ํ๋ค. ์ธก์ ์น ์ธ๋์ด ๋ค์ด์ค๋ ๊ฒฝ์ฐ, ์ธ๋์ ๋ฐฉํฅ์ ๊ณ ๋ คํ์ฌ ํ์์ฒด ๊ธฐ๋ฒ์ผ๋ก ์ธก์ ์น ๊ณต๋ถ์ฐ์ ์กฐ์ ํด์ค์ผ๋ก์จ ๋ฐฉํฅ์ ๊ณ ๋ คํ์ง ์์ ์ ์ ์ถ์ ๊ธฐ๋ฒ๋ณด๋ค ์ ํํ๊ฒ ์ธก์ ์น ์
๋ฐ์ดํธ๋ฅผ ํ ์ ์๋ค. ํนํ ์ธ๋์ด ํ ์ถ์ผ๋ก๋ง ๋ค์ด์ค๋ ๊ฒฝ์ฐ, ์ ์ํ ์๊ณ ๋ฆฌ์ฆ์ ๋ฐฉํฅ์ ๊ณ ๋ คํด ๋๋จธ์ง ๋ ์ถ์ ๋ํด์๋ ์
๋ฐ์ดํธ ํด์ค์ผ๋ก์จ ์ธก์ ์น๋ฅผ ๋ถ๋ถ์ ์ผ๋ก ์ฌ์ฉํ ์ ์๋ค. ๋ ์ดํธ ํ
์ด๋ธ, ๋ชจ์
์บก์ณ ์ฅ๋น๋ฅผ ํตํด ์ ์ํ ์๊ณ ๋ฆฌ์ฆ์ ์์ธ ์ฑ๋ฅ์ด ํฅ์๋จ์ ํ์ธํ์๋ค.
๋ค์์ผ๋ก ๋ค์ํ ๋์์์๋ ์ด์ฉ ๊ฐ๋ฅํ ์ ๋ถ ๋ฐ ๋งค๊ฐ๋ณ์ ๊ธฐ๋ฒ์ ์ตํฉํ๋ ๋ณดํํญ๋ฒ ์๊ณ ๋ฆฌ์ฆ์ ์ ์ํ๋ค. ์ค๋งํธํฐ์ ์ด์ฉํด ์ค๋ด๋ฅผ ์ด๋ํ ๋์๋ ์์ ๋๊ฐ ํฌ๊ธฐ ๋๋ฌธ์ ์ ํ ๊ฑธ๊ธฐ, ๋ฌธ์, ๋ฐ์ง ์ฃผ๋จธ๋ ๋ฃ๊ธฐ ๋ฑ ๋ค์ํ ๋์์ด ๋ฐ์ ๊ฐ๋ฅํ๋ค. ๊ธฐ์กด์ ์ค๋งํธํฐ ๊ธฐ๋ฐ ๋ณดํ ํญ๋ฒ์์๋ ๋งค๊ฐ๋ณ์ ๊ธฐ๋ฒ์ ๊ธฐ๋ฐ์ผ๋ก ์์น๋ฅผ ์ถ์ ํ๋๋ฐ, ์ด๋ ๋ณดํ์์ ์งํ ๋ฐฉํฅ๊ณผ ๊ธฐ๊ธฐ์ ๋ฐฉํฅ์ด ์ผ์นํ๋ ๊ฒฝ์ฐ์๋ง ์ฌ์ฉ ๊ฐ๋ฅํ๋ฉฐ ์ผ์นํ์ง ์๋ ๊ฒฝ์ฐ ์์ธ ์ค์ฐจ๋ก ์ธํ ์์น ์ค์ฐจ๊ฐ ํฌ๊ฒ ๋ฐ์ํ๋ค. ์ด๋ฌํ ๋ฌธ์ ๋ฅผ ํด๊ฒฐํ๊ธฐ ์ํด ๋ณธ ๋
ผ๋ฌธ์์๋ ์ ๋ถ ๊ธฐ๋ฐ ๊ธฐ๋ฒ์ ๊ธฐ๋ฐ์ผ๋ก ์ํ๋ณ์๋ฅผ ๊ตฌ์ฑํ๊ณ ๋งค๊ฐ๋ณ์ ๊ธฐ๋ฒ์ ํตํด ๋์ค๋ ์์น ๋ฒกํฐ๋ฅผ ์ธก์ ์น๋ก ์ฌ์ฉํ๋ ์๊ณ ๋ฆฌ์ฆ์ ์ ์ํ๋ค. ๋ง์ฝ ๊ธฐ๊ณํ์ต์ ํตํด ์ธ์ํ ๋์์ ๋ฐํ์ผ๋ก ์งํ ๋ฐฉํฅ๊ณผ ๊ธฐ๊ธฐ ๋ฐฉํฅ์ด ์ผ์นํ์ง ์๋ ๊ฒฝ์ฐ, ์ฃผ์ฑ๋ถ ๋ถ์์ ํตํด ๊ณ์ฐํ ์งํ๋ฐฉํฅ์ ์ด์ฉํด ์งํ ๋ฐฉํฅ์, ๋งค๊ฐ๋ณ์ ๊ธฐ๋ฒ์ ํตํด ์ป์ ๋ณดํญ์ผ๋ก ๊ฑฐ๋ฆฌ๋ฅผ ์
๋ฐ์ดํธํด ์ค์ผ๋ก์จ ๋ณดํ ์ค ๋ฐ์ํ๋ ์ฌ๋ฌ ๋์์์๋ ๊ฐ์ธํ๊ฒ ์ด์ฉํ ์ ์๋ค.
๋ค์ํ ๋์ ์ํฉ ๋ฐ ๊ฒฝ๋ก๋ฅผ ๊ณ ๋ คํ ์คํ์ ํตํด ์์์ ์ ์ํ ๋ฐฉ๋ฒ์ด ๋ค์ํ ์ค๋ด ํ๊ฒฝ์์๋ ์์ ์ ์ผ๋ก ์์น๋ฅผ ์ถ์ ํ๊ณ ์ฑ๋ฅ์ด ํฅ์๋จ์ ํ์ธํ์๋ค.Chapter 1 Introduction 1
1.1 Motivation and Background 1
1.2 Objectives and Contribution 5
1.3 Organization of the Dissertation 6
Chapter 2 Pedestrian Dead Reckoning System 8
2.1 Overview of Pedestrian Dead Reckoning 8
2.2 Parametric Approach 9
2.2.1 Step detection algorithm 11
2.2.2 Step length estimation algorithm 13
2.2.3 Heading estimation 14
2.3 Integration Approach 15
2.3.1 Extended Kalman filter 16
2.3.2 INS-EKF-ZUPT 19
2.4 Activity Recognition using Machine Learning 21
2.4.1 Challenges in HAR 21
2.4.2 Activity recognition chain 22
Chapter 3 Attitude Estimation in Smartphone 26
3.1 Adaptive Attitude Estimation in Smartphone 26
3.1.1 Indirect Kalman filter-based attitude estimation 26
3.1.2 Conventional attitude estimation algorithms 29
3.1.3 Adaptive attitude estimation using ellipsoidal methods 30
3.2 Experimental Results 36
3.2.1 Simulation 36
3.2.2 Rate table experiment 44
3.2.3 Handheld rotation experiment 46
3.2.4 Magnetic disturbance experiment 49
3.3 Summary 53
Chapter 4 Pedestrian Dead Reckoning in Multiple Poses of a Smartphone 54
4.1 System Overview 55
4.2 Machine Learning-based Pose Classification 56
4.2.1 Training dataset 57
4.2.2 Feature extraction and selection 58
4.2.3 Pose classification result using supervised learning in PDR 62
4.3 Fusion of the Integration and Parametric Approaches in PDR 65
4.3.1 System model 67
4.3.2 Measurement model 67
4.3.3 Mode selection 74
4.3.4 Observability analysis 76
4.4 Experimental Results 82
4.4.1 AHRS results 82
4.4.2 PCA results 84
4.4.3 IA-PA results 88
4.5 Summary 100
Chapter 5 Conclusions 103
5.1 Summary of the Contributions 103
5.2 Future Works 105
๊ตญ๋ฌธ์ด๋ก 125
Acknowledgements 127Docto
Survey of Motion Tracking Methods Based on Inertial Sensors: A Focus on Upper Limb Human Motion
Motion tracking based on commercial inertial measurements units (IMUs) has been widely studied in the latter years as it is a cost-effective enabling technology for those applications in which motion tracking based on optical technologies is unsuitable. This measurement method has a high impact in human performance assessment and human-robot interaction. IMU motion tracking systems are indeed self-contained and wearable, allowing for long-lasting tracking of the user motion in situated environments. After a survey on IMU-based human tracking, five techniques for motion reconstruction were selected and compared to reconstruct a human arm motion. IMU based estimation was matched against motion tracking based on the Vicon marker-based motion tracking system considered as ground truth. Results show that all but one of the selected models perform similarly (about 35 mm average position estimation error)
A methodology for the performance evaluation of inertial measurement units
This paper presents a methodology for a reliable comparison among Inertial Measurement Units or attitude estimation devices in a Vicon environment. The misalignment among the reference systems and the lack of synchronization among the devices are the main problems for the correct performance evaluation using Vicon as reference measurement system. We propose a genetic algorithm coupled with Dynamic Time Warping (DTW) to solve these issues. To validate the efficacy of the methodology, a performance comparison is implemented between the WB-3 ultra-miniaturized Inertial Measurement Unit (IMU), developed by our group, with the commercial IMU InertiaCube3โข by InterSense
Human motion tracking based on complementary Kalman filter
Miniaturized Inertial Measurement Unit (IMU) has been widely used in many motion capturing applications. In order to overcome stability and noise problems of IMU, a lot of efforts have been made to develop appropriate data fusion method to obtain reliable orientation estimation from IMU data. This article presents a method which models the errors of orientation, gyroscope bias and magnetic disturbance, and compensate the errors of state variables with complementary Kalman filter in a body motion capture system. Experimental results have shown that the proposed method significantly reduces the accumulative orientation estimation errors
Sliding Mode Control
The main objective of this monograph is to present a broad range of well worked out, recent application studies as well as theoretical contributions in the field of sliding mode control system analysis and design. The contributions presented here include new theoretical developments as well as successful applications of variable structure controllers primarily in the field of power electronics, electric drives and motion steering systems. They enrich the current state of the art, and motivate and encourage new ideas and solutions in the sliding mode control area
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