• Title/Summary/Keyword: Joint angle estimation model

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A Study on the Effect of Weighting Matrix of Robot Vision Control Algorithm in Robot Point Placement Task (점 배치 작업 시 제시된 로봇 비젼 제어알고리즘의 가중행렬의 영향에 관한 연구)

  • Son, Jae-Kyung;Jang, Wan-Shik;Sung, Yoon-Gyung
    • Journal of the Korean Society for Precision Engineering
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    • v.29 no.9
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    • pp.986-994
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    • 2012
  • This paper is concerned with the application of the vision control algorithm with weighting matrix in robot point placement task. The proposed vision control algorithm involves four models, which are the robot kinematic model, vision system model, the parameter estimation scheme and robot joint angle estimation scheme. This proposed algorithm is to make the robot move actively, even if relative position between camera and robot, and camera's focal length are unknown. The parameter estimation scheme and joint angle estimation scheme in this proposed algorithm have form of nonlinear equation. In particular, the joint angle estimation model includes several restrictive conditions. For this study, the weighting matrix which gave various weighting near the target was applied to the parameter estimation scheme. Then, this study is to investigate how this change of the weighting matrix will affect the presented vision control algorithm. Finally, the effect of the weighting matrix of robot vision control algorithm is demonstrated experimentally by performing the robot point placement.

An Experimental Study on the Optimal Arrangement of Cameras Used for the Robot's Vision Control Scheme (로봇 비젼 제어기법에 사용된 카메라의 최적 배치에 대한 실험적 연구)

  • Min, Kwan-Ung;Jang, Wan-Shik
    • Journal of the Korean Society of Manufacturing Technology Engineers
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    • v.19 no.1
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    • pp.15-25
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    • 2010
  • The objective of this study is to investigate the optimal arrangement of cameras used for the robot's vision control scheme. The used robot's vision control scheme involves two estimation models, which are the parameter estimation and robot's joint angle estimation models. In order to perform this study, robot's working region is divided into three work spaces such as left, central and right spaces. Also, cameras are positioned on circular arcs with radius of 1.5m, 2.0m and 2.5m. Seven cameras are placed on each circular arc. For the experiment, nine cases of camera arrangement are selected in each robot's work space, and each case uses three cameras. Six parameters are estimated for each camera using the developed parameter estimation model in order to show the suitability of the vision system model in nine cases of each robot's work space. Finally, the robot's joint angles are estimated using the joint angle estimation model according to the arrangement of cameras for robot's point-position control. Thus, the effect of camera arrangement used for the robot's vision control scheme is shown for robot's point-position control experimentally.

Estimation of Knee Muscle Length and Moment Arm Using Knee Joint Angle (무릎 관절각을 이용한 무릎 근육 길이와 모멘트 암 추정)

  • Lee, Jae-Kang;Nam, Yoon-Su
    • Journal of Industrial Technology
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    • v.28 no.A
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    • pp.167-176
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    • 2008
  • Recently, lots of studies are performed in developing of active orthosis. Exact and simple muscle force estimation is important in developing orthosis which assists muscle force for disabled people or physical laborers. Hill-type muscle model dynamics is common method for estimation of muscle forces. In Hill-type muscle model, we must know muscle length and moment arm which largely affect muscle force. And several methods are proposed to estimate muscle length and moment arm using joint angle. In this study, we compared estimation results of those method with data from body model of opensim to find which method is exact for estimation of muscle length and moment arm.

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팔의 자세예측을 위한 비용함수의 개발에 관한 연구

  • 최재호;김성환;정의승
    • Proceedings of the ESK Conference
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    • 1994.04a
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    • pp.115-123
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    • 1994
  • A man model can be used as an effective tool to design ergomonically sound products and workplaces, and subsequently evaluate them properly. For a man model to be truly useful, it must be integrated with a posture prediction model which should be capable of representing the human arm reach posture in the context of equipments and workspaces. Since the human movement possesses redundant degrees of freedom, accurate representation or prediction of human movemtn was known to be a difficult problem. To solve this redundancy problem, the psychophysical cost function can predict the arm reach posture accurately. But the joint discomfort that human feels at the joint can not be predicted since the effects of external factors on the joint discomfort is not known. In this study a psychophysical experi- ment using the magnitude estimation technique was performed to evaluate the effects of external factors such as joint, joint angle and Perceived Exertion Ratio on the joint discomfort. Results showed that the joint discomfort increased as the Perceived Exertion Ratio increased, but the relation is not linear and was affected not only by the joint but also by the joint angle for the same Perceived Exertion Ratio. The interaction effect of the joint and the joint angle was also significant. From the results it is needed to develope the cost function which can predict the joint discomfort considering the joint, joint angle and external load.

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Development of a Musculoskeletal Model for Functional Electrical Stimulation - Noninvasive Estimation of Musculoskeletal Model Parameters at Knee Joint - (기능적 전기자극을 위한 근골격계 모델 개발 - 무릎관절에서의 근골격계 모델 특성치의 비침습적 추정 -)

  • 엄광문
    • Journal of Biomedical Engineering Research
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    • v.22 no.3
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    • pp.293-301
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    • 2001
  • A patient-specific musculoskeletal model, whose parameters can be identified noninvasively, was developed for the automatic generation of patient-specific stimulation pattern in FES. The musculotendon system was modeled as a torque-generator and all the passive systems of the musculotendon working at the same joint were included in the skeletal model. Through this, it became possible that the whole model to be identified by using the experimental joint torque or the joint angle trajectories. The model parameters were grouped as recruitment of muscle fibers, passive skeletal system, static and dynamic musculotendon systems, which were identified later in sequence. The parameters in each group were successfully estimated and the maximum normalized RMS errors in all the estimation process was 8%. The model predictions with estimated parameter values were in a good agreement with the experimental results for the sinusoidal, triangular and sawlike stimulation, where the normalized RMS error was less than 17%, Above results show that the suggested musculoskeletal model and its parameter estimation method is reliable.

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Fast-convergence trilinear decomposition algorithm for angle and range estimation in FDA-MIMO radar

  • Wang, Cheng;Zheng, Wang;Li, Jianfeng;Gong, Pan;Li, Zheng
    • ETRI Journal
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    • v.43 no.1
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    • pp.120-132
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    • 2021
  • A frequency diverse array (FDA) multiple-input multiple-output (MIMO) radar employs a small frequency increment across transmit elements to produce an angle-range-dependent beampattern for target angle and range detection. The joint angle and range estimation problem is a trilinear model. The traditional trilinear alternating least square (TALS) algorithm involves high computational load due to excessive iterations. We propose a fast-convergence trilinear decomposition (FC-TD) algorithm to jointly estimate FDA-MIMO radar target angle and range. We first use a propagator method to obtain coarse angle and range estimates in the data domain. Next, the coarse estimates are used as initialized parameters instead of the traditional TALS algorithm random initialization to reduce iterations and accelerate convergence. Finally, fine angle and range estimates are derived and automatically paired. Compared to the traditional TALS algorithm, the proposed FC-TD algorithm has lower computational complexity with no estimation performance degradation. Moreover, Cramer-Rao bounds are presented and simulation results are provided to validate the proposed FC-TD algorithm effectiveness.

Development of a Model for the Estimation of Knee Joint Moment at MVC (MVC 상태에서의 무릎관절 모멘트 추정을 위한 모델 개발)

  • Nam, Yoon-Su;Lee, Woo-Eun
    • Journal of Biomedical Engineering Research
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    • v.29 no.3
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    • pp.222-230
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    • 2008
  • This paper introduces a method of estimating the knee joint moment developed during MVC. By combining the Hill-type muscle model and analytic results on moment arm and musculotendon length change as a function of hip and knee joint angle, the knee joint moment at a specific knee joint angle during MVC is determined. Many differences between the estimated results and the experimental data are noted. It is believed that these differences originate from inaccurate information on the muscle-tendon parameters. The establishment of exact values for the subject's muscle parameters is almost impossible task. However, sensitivity analysis shows that the tendon slack length is the most critical parameter when applying the Hill-type muscle model. The effect of a change of this parameter on the muscle length force relationship is analyzed in detail.

Angle-Range-Polarization Estimation for Polarization Sensitive Bistatic FDA-MIMO Radar via PARAFAC Algorithm

  • Wang, Qingzhu;Yu, Dan;Zhu, Yihai
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.14 no.7
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    • pp.2879-2890
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    • 2020
  • In this paper, we study the estimation of angle, range and polarization parameters of a bistatic polarization sensitive frequency diverse array multiple-input multiple-output (PSFDA-MIMO) radar system. The application of polarization sensitive array in receiver is explored. A signal model of bistatic PSFDA-MIMO radar system is established. In order to utilize the multi-dimensional structure of array signals, the matched filtering radar data can be represented by a third-order tensor model. A joint estimation of the direction-of-departure (DOD), direction-of-arrival (DOA), range and polarization parameters based on parallel factor (PARAFAC) algorithm is proposed. The proposed algorithm does not need to search spectral peaks and singular value decomposition, and can obtain automatic pairing estimation. The method was compared with the existing methods, and the results show that the performance of the method is better. Therefore, the accuracy of the parameter estimation is further improved.

Study on the Estimation Model of Shear Strength at Rock Joint and Its Influence Factor (암석절리면 전단강도 예측모델 및 영향요소에 관한 연구)

  • Son, Moorak
    • Journal of the Korean Geotechnical Society
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    • v.39 no.5
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    • pp.5-12
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    • 2023
  • This study investigates the existing models for estimating the shear strength of rock joints, presents related problems, and introduces a newly proposed model to overcome the problems. The results of many experimental tests show that the shear strength of a rock joint depends on many complex factors, including asperity angle, compressive strength, applied normal stress, friction angle, asperity cohesive strength, and progressive damage of asperities. However, the existing models do not account for these factors enough. To overcome these problems, Son (2020) developed a new model to estimate the shear strength of rock joints and confirmed its reliability by comparing with experimental results and existing models. In this paper, the developed model was used to investigate the various factors that affect the joint shear strength, and the results were compared and analyzed. Through this study, the factors that affect the shear strength of the rock joint could be identified in more detail.

Real-time Robotic Vision Control Scheme Using Optimal Weighting Matrix for Slender Bar Placement Task (얇은 막대 배치작업을 위한 최적의 가중치 행렬을 사용한 실시간 로봇 비젼 제어기법)

  • Jang, Min Woo;Kim, Jae Myung;Jang, Wan Shik
    • Journal of the Korean Society of Manufacturing Technology Engineers
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    • v.26 no.1
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    • pp.50-58
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    • 2017
  • This paper proposes a real-time robotic vision control scheme using the weighting matrix to efficiently process the vision data obtained during robotic movement to a target. This scheme is based on the vision system model that can actively control the camera parameter and robotic position change over previous studies. The vision control algorithm involves parameter estimation, joint angle estimation, and weighting matrix models. To demonstrate the effectiveness of the proposed control scheme, this study is divided into two parts: not applying the weighting matrix and applying the weighting matrix to the vision data obtained while the camera is moving towards the target. Finally, the position accuracy of the two cases is compared by performing the slender bar placement task experimentally.