2,035 research outputs found
The application of virtual sampling in visual servo tracking system
No full text由於影像伺服追蹤系統受限於攝影機較低的取樣速度，造成整體追蹤性能不佳。在本文中，我們提出適應性預測及內插方法。藉由此方法，得到高取樣速度下目標物運動軌跡的虛擬取樣位置，並配合伺服馬達做高速度取樣的追蹤控制，以改善整體追蹤效能。
結果顯示，使用高階的適應性線性軌跡模型做預測，配合曲線內插方法可估測出較準確的物體運動軌跡。一個低取樣速度系統，在加入預測性內插後，其追蹤效能近似高取樣速度系統。The tracking performance of visual servo system is limited by the low sampling rate of camera. In this thesis, we present an adaptive prediction and interpolation method. Through this method, we could get the virtual sampling values of motion trajectory of target at high sampling rate. According to the high sampling rate trajectory, the servo motor could perform high speed tracking control.
The results show that we could get more accurate motion trajectory by using high order linear model for adaptive estimation. In addition, the tracking performance of low sampling rate system in cooperated with the predictive interpolation will approach that of high sampling rate system.CATALOG
摘要 I
Abstract II
Chapter 1 Introduction 1
1.1 Visual servo system 1
1.2 Problem of visual servo system 3
1.3 Thesis organization 4
Chapter 2 Estimation of motion Trajectory 6
2.1 Recursive least squares and projection algorithm 6
2.1.1 Regression form 6
2.1.2 Recursive least squares estimation 8
(a) Recursive least squares algorithm 8
(b) Recursive least squares algorithm with exponential forgetting 9
(c) Recursive projection algorithm 9
2.2 Model based prediction 10
2.3 Curve fitting 11
2.4 Prediction of motion trajectory 13
2.4.1 A linearized Pendulum model 13
2.4.2 Prediction with different order model 16
(a) order 3 16
(b) order 5 20
(c) order 7 24
(d) Comparison of different methods with different order 28
2.5 Interpolation 30
2.5.1 Linear Interpolation 31
2.5.2 Curve Interpolation 32
Chapter 3 The structure of visual servo system 36
3.1 Overview of visual servo system 36
3.2 Build the model of visual servo system 37
3.3 Controller design 39
3.3.1 Continuous-time controller design 39
3.3.2 Discrete-time equivalent controller 43
3.4 Predictive interpolation 49
3.4.1 The structure of Predictive interpolation 49
3.4.2 Comparison of simulation 53
3.4.3 The problem about delay of image processing 57
Chapter 4 Experiment 61
4.1 Hardware and software 61
4.2 Calibration of camera parameters 63
4.3Target Detection and Graphic User Interface(GUI) 71
4.3.1 Target Detection Methods 71
4.3.2 Introduction of Graphic User Interface (GUI) 77
4.4 Tracking performance comparison 82
Chapter 5 Discussion and future work 86
Reference 88
FIGURE
Figure 2. 1 Pendulum model 13
Figure 2. 2 Continuous motion trajectory in x direction 14
Figure 2. 3 Sampled trajectory signal 15
Figure 2. 4 Prediction with LS Exp (order3) 17
Figure 2. 5 Prediction with projection (order3) 18
Figure 2. 6 Prediction with curve fitting (order3) 19
Figure 2. 7 Prediction with LS Exp (order5) 21
Figure 2. 8 Prediction with Projection (order5) 22
Figure 2. 9 Prediction with curve fitting (order5) 23
Figure 2. 10 Prediction with LS Exp (order7) 25
Figure 2. 11 Prediction with projection (order7) 26
Figure 2. 12 Prediction with curve fitting (order7) 27
Figure 2. 13 Prediction error comparison of LS Exp with different order 28
Figure 2. 14 Prediction error comparison of Projection with different order 28
Figure 2. 15 Prediction error comparison of curve fitting with different order 29
Figure 2. 16 Interpolation 30
Figure 2. 17 Linear interpolation 31
Figure 2. 18 Interpolation with linear method 32
Figure 2. 19 Curve interpolation 33
Figure 2. 20 (a)(b) 34
Figure 2. 21 Interpolation with curve method 35
Figure 2. 22 Interpolation error comparison 35
Figure 3. 1 36
Figure 3. 2 Geometry relationship with pinhole perspective projection 38
Figure 3. 3 Block diagram of visual servo system 39
Figure 3. 4 Continuous version of visual servo system 39
Figure 3. 5 Root locus of the system with PI controller 40
Figure 3. 6 Step response with kp=50 41
Figure 3. 7 Ramp response with kp=50 42
Figure 3. 8 Sampled data system with digital control 44
Figure 3. 9 Step response with different sampling rate 44
Figure 3. 10 Root locus in Z-domain with 0.5sec sampling period 45
Figure 3. 11 Root locus in Z-domain with 0.1sec sampling period 46
Figure 3. 12 Root locus in Z-domain with 0.05sec sampling period 46
Figure 3. 13 Root locus in Z-domain with 0.01sec sampling period 47
Figure 3. 14 Tracking errors of different sampling periods with ultimate gains applied 48
Figure 3. 15 The original structure of visual servo system 49
Figure 3. 16 visual servo system with the predictive interpolation type 1 50
Figure 3. 17 visual servo system with the predictive interpolation type 2 51
Figure 3. 18 virtual loop system 52
Figure 3. 19 Visual servo system with 0.1 sec sampling period 53
Figure 3. 20 Visual servo system with 0.01 sec sampling period 53
Figure 3. 21 Visual servo system with predictive interpolation 54
Figure 3. 22 Tracking simulation with 0.1sec sampling rate 54
Figure 3. 23 Tracking simulation with 0.01sec sampling rate 55
Figure 3. 24 Tracking simulation with interpolation 55
Figure 3. 25 Interpolation error 56
Figure 3. 26 57
Figure 3. 27 58
Figure 3. 28 58
Figure 3. 29 60
Figure 4. 1 61
Figure 4. 2 62
Figure 4. 3 63
Figure 4. 4 65
Figure 4. 5 66
Figure 4. 6 66
Figure 4. 7 69
Figure 4. 8 71
Figure 4. 9 72
Figure 4. 10 72
Figure 4. 11 73
Figure 4. 12 73
Figure 4. 13 (a)(b)(c) 74
Figure 4. 14 (a)(b) 75
Figure 4. 15 (a)(b) 75
Figure 4. 16 (a)(b) 75
Figure 4. 17 76
Figure 4. 18 76
Figure 4. 19 77
Figure 4. 20 78
Figure 4. 21 79
Figure 4. 22 79
Figure 4. 23 80
Figure 4. 24 81
Figure 4. 25 81
Figure 4. 26 82
Figure 4. 27 82
Figure 4. 28 (a)(b) 83
Figure 4. 29 Tacking without prediction 84
Figure 4. 30 Tacking without prediction 84
Figure 4. 31 Tacking with prediction 85
Figure 4. 32 Tacking with prediction 85
TABLE
Table 4. 1 67
Table 4. 2 68
Table 4. 3 7
New Algorithms for Robust Parameter Identification and Time-Variant Parameter Identification
No full text本論文主要在探討連續時間參數識別的兩大主題，一為遭受非隨機
干擾下的參數識別，另一為時變參數的識別。除了量側雜訊外，一般
系統的輸出通常會被干擾所汙染，而這些干擾包含感測器的誤差、模
型誤差以及系統遭受外部擾動而引發。大部分的系統識別方法僅考慮
干擾為白雜訊，當出現以上的干擾時會造成參數估側偏差。在作參數
化時，我們可以將不同來源的干擾總和於一個干擾於輸出端。在本論
文中， 我們將提出一個離線識別以及兩個線上及時識別的方法來處理
此問題。
在離線識別的方法中，未知的干擾將由一有限項的傅立葉餘弦級數
來表示。此級數係數為未知。藉由結合級數的基底函數及原本的回歸
向量(regressor) 可得到一組擴展的回歸向量，藉由最小平方法做批量計
算，即可得到包含係數及參數在內的估測值。最後並提出此擴展的回
歸向量於持續刺激性(persistent excitation)的必要條件。
在第一個線上估測方法中，其架構是建立在梯度演算法上。在干擾
的影響下，為了使參數的誤差方程式可以收斂到零，必須作額外的控
制補償。於控制設計上，將使用平均法來得到近似系統，並利用H1頻
率成型來合成控制器。此控制訊號將可追蹤干擾訊號並將之抵消，因
此可保證估測參數可收斂到正確值。
第二個線上估測的方法為使用狀態估測器。為了將干擾納入估測器
作估測，於此我們提出一種干擾產生濾波器，並將其模型加入原參數
狀態方程式中。利用卡曼濾波器(Kalman filter)作狀態估測。相對於傳
統的內部模型法(internal model approach)，此新方法將可適用於更廣泛
類型的干擾。以上提出的三個方法可同時估測出參數及干擾。
以上兩種線上估測的設計方法經過一些調整後可運用在時變參數識
別的問題上。其細節將於本文中作描述。
關鍵字: 強韌參數識別，時變參數識別，干擾識別，卡曼濾波器。Two subjects of continuous-time parameter identification problems expressed in linear regression form are discussed in this thesis. One is the time-invariant parameter identification while subject to non-stochastic disturbances termed as the robust identification. The other is the time-variant parameter identification.
In addition to the measurement stochastic noise, the output signal of a system is usually contaminated with the non-stochastic disturbances which are usually resulted from errors of measure devices, system unmodled dynamics or the process disturbances acting on the system. Most identifications considering the disturbance as a white noise will have biased estimates while subject to these kinds of disturbances. In the parameterization, one can lump all the disturbances into one disturbance term at the output expressed in linear regression form. We proposes one off-line approach and two on-line approaches to deal with this problem.
In the off-line approach, the unknown disturbance will be approximately expanded by a finite Fourier cosine series with unknown coefficients. The unknown coefficients and the known basis functions will be augmented to the original parameter vector and the regressor respectively. With the expanded regressor, one can obtain the estimates of the expanded parameter vector by adopting the least-squares batch calculation. A necessary condition on persistent excitation of the expanded regressor is proposed too.
In the first of the two on-line approaches, the estimation scheme is built under the structure of gradient algorithm. A compensation is made to reject the effect of the disturbance in the estimation error dynamics by designing a stabilized controller. In the design procedure, the averaging method is used for system approximation and the frequency shaping methodology is utilized to synthesize the controller. The control signal will be able to track the disturbance signal and cancel it in the estimation error dynamics and that guarantees the convergence of the parameter estimation.
In the second of the on-line approaches, an state-observer based estimator is constructed. To include the estimation of the disturbance into the estimation scheme, the system plant is augmented with the model of the proposed disturbance generating filter also termed as dynamics extension filter. The Kalman filter is adopted to perform the states estimation. Compared with the conventional internal model approach, the proposed method could be applied to a more general disturbance class. The three proposed approaches can identify both parameters and the disturbance simultaneously.
The design procedures of the above two on-line approaches can be grafted to the time-variant parameter identification problem with some modifications. Special consideration will be addressed in the context.
Keywords: Robust identification, Time-variant parameter identification, Disturbance
identification, Kalman filter
Road Detection and Classification in Urban Environments Using Conditional Random Field Models
No full textEvaluating safety at railway level crossings with microsimulation modeling
Get PDFSafety at railway level crossings (RLXs) is a worldwide issue that increasingly attracts the attention of relevant transport authorities, the rail industry, and the general public. The differences in the operation characteristics of varying types of warning devices, together with differences in crossing geometry, traffic, or train characteristics, leads to different driver behaviors at crossings. The aim of this study was to use traffic microsimulation modeling based on field video recording data to compare the safety performance of varying conventional RLX warning systems. The widely used microsimulation model VISSIM was modified to produce safety-related performance measures, namely, collision likelihood, delay, and queue length. The results showed that RLXs with an active warning system were safer than those with a passive sign by at least 17%. Integration of surrogate measures in conjunction with traffic simulation models determined which safety approach was more efficient for specified traffic and train volumes
Life Cycle Cost Analysis of Maintenance Costs and Budgets for University Buildings in Taiwan
No full textSian (China), close-up of roof of the Great Mosque of Xian
No full textThe great mosque of Li Pai Sze in Sian, Shensi [Shaanxi] province. Sian was the most important city in China for more than 2,000 years. From as far back as 1122 B.C. it served as the Imperial Capital for the greatest and most illustrious of China's dynasties, including the Western Chou, the Chin, the Han and the Tang. Islam flourished here under the Imperial guarantee for religious freedom. The powerful Chinese influence, however, is evident in the Chinese-style architecture (carved [illegible] tiled roof, upturned eaves) employed in [illegible] construction of this mosque which is believed to be the oldest in China. The Chinese characters under the eaves read: I Cheng -- 'One Truth'Hong Kong? 1937-02? [Photographer's Note incorrect] Sian, Shaanxi provinceThe Great Mosque of Xian was built during the Tang dynasty in 742 as the religious center for Chinese Muslims in Xian.Great Mosque, Xian, China. (2012). Asian Historical Architecture. Retrieved from http://www.orientalarchitecture.com/china/xian/greatmosque.phpGrayscaleForman Nitrate Negatives, Box 2
What is the impact of teaching culturally diverse graphic novels on students of KS3 English? A Case Study
No full textGraphic novels are an increasingly popular choice for young readers, sparking debate about whether they are appropriate taught material in the secondary English classroom. Meanwhile, a separate debate has been focused on decolonising the English curriculum to be more representative of different identities, especially races. This interventionist case study examines how graphic novels could sit at the intersection of these two movements, diversifying both the characters and forms taught in the KS3 curriculum. The study reveals that graphic novels support student engagement, as pupils find them interesting, informative and easier to understand than traditional prose texts. Within the case study, students’ analytical writing on the graphic novel was more accurate and detailed than the responses they wrote to the prose text, aided by Moebius’s (1990) picturebook codes. The visual elements of the graphic novel not only supported comprehension, but also provided additional clues about characters and their lives; pupils seemed to connect more with the characters in the graphic novel. The study also proposes that cultural awareness could be improved through teaching graphic novels, although teaching a range of texts would promote this further. The author suggests that graphic novels are an untapped resource for the KS3 curriculum and encourages English teachers to trial them in their classrooms
Integration of driving simulator and traffic simulation to analyse behavior at railway crossings
No full textThe use of state-of-the-art technology to collect and analyse data has significantly improved the effectiveness of safety studies. Currently, despite the fact that there are many safety systems deployed at railway crossings, only limited research has been conducted to evaluate which of these systems is the most effective in terms of costs and safety. This paper demonstrates a way to evaluate safety at railway crossings using a twin-pronged approach: a driving simulator and traffic simulation software. A number of outputs have been observed from a driving simulator, such as driver compliance rate, vehicle speed profile, acceleration profile, initial braking position and final braking position. The compliance percentage at passive crossings (67 and 72% for a stop sign and rumble strips, respectively) has lower compliance rates compared with active crossings (97 and 93% for flashing red light and in-vehicle audible warning, respectively) at an 80km/h approach speed. Using a statistical analysis it is shown that speed and acceleration profiles can be used to differentiate the effectiveness of active and passive crossings. These indicators are interpreted and used as input to a traffic simulation, which assists in determining which safety device is more efficient. By integrating driving simulator and traffic simulation models, this approach can be applied to evaluate and compare safety performance without the need to install costly test beds at real railway crossings
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