Monthly Archives: June 2013

Previous research

>> 3D Environment Reconstruction & 3D SLAM based on Plane Feature

- Period : September, 2010 ~  2012

- Co-research with Prof. Nakju Doh, and Sooyong Yeon, Robotics Lab., Korea University
- Feature-based 3D SLAM(Simultaneous Localization and Mapping) algorithms can overcome weak points of non-feature-based (point-based) 3D SLAM algorithm such as ICP. Plane feature-based 3D SLAM approach manages map with plane features, which enables 3D SLAM at large-scale environment in real time.

* click to see a large picture

- Related Papers:

Sooyong Yeon, Changhyun Jun, Hyunga Choi, Jaehyeon Kang, Youngmok Yun, and Nakju Lett Doh, “Robust-PCA based Hierarchical Plane Extraction for the Application of Geometric 3D SLAM,” Industrial Robot, in Press

>> Outdoor Multi-robot Formation Control

- Period : July, 2011 ~
– Project of Agency for Defense Development(ADD), directed by Dr. Changhwan Kim, Korea Institute of Science and Technology(KIST)
- The goal of this project is first to control outdoor multi-robot with particular formations. Additionally, this control algorithm will be fused with other various applications such as surveillance, and invader searching.

- Application : Cooperative Control For Optimal Surveillance

YouTube 동영상

YouTube 동영상


>> Outdoor Localization with Optical Navigation Sensor, IMU and GPS 

Autonomous outdoor navigation algorithms are required in various military and industry fields. A stable and robust outdoor localization algorithm is critical to successful outdoor
navigation. However, unpredictable external effects and interruption of the GPS signal cause difficulties in outdoor localization. To address this issue, first we devised a new optical navigation
sensor that measures a mobile robot’s transverse distance without being subjected to external influence. Next, using the optical navigation sensor, a novel localization algorithm is established with Inertial-Measurement-Unit (IMU) and GPS. The algorithm is verified in an urban environment where the GPS signal is frequently interrupted and rough ground surfaces provide serious disturbances.

outdoor_mobile_robot outdoor_localization

- Related Papers:

Youngmok Yun, Jingfu Jin, Namhoon Kim, Jeongyeon Yoon, and Changhwan Kim, “Outdoor localization with optical navigation sensor, IMU and GPS,” Int. Conf. on Multisensor Fusion and Integration for Intelligent Systems (MFI), pp. 377-382. IEEE, 2012.


>> Mechatronic System Design and Control for Automobile System Validation 

- Period : April, 2008 ~ June, 2011

- Assistant Researcher, Renault-Samsung Motors Technical Center
– As a military service, for three years, I had worked at the Renault-Samsung Motors Technical Center. Main tasks were mechatronic system design and control for validation of automobile chassis system. Through these experience, I could learn about the practical design sense of mechanical and electronic system. Additionally, global cooperation with world-wide coworkers and participation to several huge-scale automobile projects were another pleasure. 

< Steering Performance Test Bench >

< Acc., Brake, Clutch Pedal & Parking Brake Durability Test Bench >

 >> Odometry Calibration of Mobile Robot using Home-positioning

- Period : 2007

- Advisor : Prof. Wankyun Chung, Robotics Lab., POSTECH
- The subject of  MS graduation thesis was “Odometry calibration of mobile robot using home-positioning.” Home-positioning frequently occurs due to the reason of recharge or initialization of mobile robot. What is even better is that its technique is maturely developed and already adopted in many commercial robots. My ideas was that home-positioning can be used as a powerful measurement of mobile robot, and this would lead high accuracy of odometry. You may see the detail idea at the below poster.



- Related Papers:

- Youngmok Yun, Byungjae Park, and Wan Kyun Chung, “Odometry Calibration using Home Positioning Function for Mobile Robot,” IEEE Int. Conf. on Robotics and Automation (ICRA), 2008.

- Sharifuddin Mondal, Youngmok Yun, and Wan Kyun Chung, “Terminal Iterative Learning Control for Calibrating Systematic Odometry Errors in Mobile Robots,” IEEE/ASME Int. Conf. on Advanced Intelligent Mechatronics (AIM), 2010.

- Youngmok Yun, Wan Kyun Chung, and Sang yep Nam, “Systematic Error Calibration of Mobile Robot using Home Positioning Function,” IEEE Int. Conf. on Ubiquitous Robots and Ambient Intelligence (URAI), 2008.

>> Development of Navigation and Human-robot Interaction Techniques 

- Period : 2006 ~ 2007
- Director : Prof. Wankyun Chung, Robotics Lab., POSTECH
- Project of Korean Ministry of Information and Communication
- Mobile robot team, Robotics Lab, POSTECH, developed autonomous mobile robot framework including SLAM, exploration, and path-planning. I took part in SLAM algorithm development and visual-feature recognition. 

SLAM movie clip, Robotics Lab, POSTECH


>> Tennis ball gathering robot using visual recognition

- Period : 2004 ~ 2005
- Graduation project, Mechanical Engineering Dept., POSTECH 
- Winner of the graduation project. 
- As a graduation project of ME, POSTECH, our team made tennis ball gathering robot using visual recognition. The task of the robot was to gather all tennis balls in a room. Frankly, now I can know that it was too difficult subject for undergraduate students to build all system and programs only within one year because there were too many things to do: mechanical design, electronic system organization, visual recognition programming, navigation algorithm programming and others. However, we devoted ourselves to the project, and finally, we won the competition of the project. 


Statistical Method for Prediction of Human Walking Pattern with Gaussian Process Regression

Statistical Method for Prediction of Human Walking Pattern with Gaussian Process Regression

We propose a novel methodology for predicting human gait pattern kinematics based on a statistical and stochastic approach using a method called Gaussian process regression (GPR). We selected 14 body parameters that significantly affect the gait pattern and 14 joint motions that represent gait kinematics. The body parameter and gait kinematics data were recorded from 113 subjects by anthropometric measurements and a motion capture system. We generated a regression model with GPR for gait pattern prediction and built a stochastic function mapping from body parameters to gait kinematics based on the database and GPR, and validated the model with a cross validation method. The function can not only produce trajectories for the joint motions associated with gait kinematics, but can also estimate the associated uncertainties. Our approach results in a novel, low-cost and subject-specific method for predicting gait kinematics with only the subject’s body parameters as the necessary input, and also enables a comprehensive understanding of the correlation and uncertainty between body parameters and gait kinematics.




Yun, Youngmok; Kim, Hyun-Chul; Shin, Sung Yul; Lee, Junwon; Deshpande, Ashish D; Kim, Changhwan

Statistical method for prediction of gait kinematics with Gaussian process regression Journal Article

Journal of Biomechanics, 47 (1), pp. 186 - 192, 2014, ISSN: 0021-9290.

Links | BibTeX


Yun, Youngmok; Deshpande, Ashish D

Synthesis of Gait Kinematics Using a Database Conference

Dynamic Walking, 2013.


Yun, Youngmok; Deshpande, Ashish

Statistical Functional Mapping From Body Parameters to Gait Kinematics Conference

American Society of Biomechanics, 2013.



Sliding Mode Control (SMC), Robust control algorithm for nonlinear system

In this article, I am going to write about the Sliding Mode Control (SMC) algorithm.

Sliding Mode Control algorithm is a robust controller for nonlinear systems.

Robust control means that even though the system model has a certain error, if the controller can control the system, we say that the controller is robust.

SMC has two fundamental ideas.

1. To attract the system states to the surface.

2. To make the state slide on the surface toward the origin.

To explain the above two ideas more easily, let’s assume a typical control problem.

\dot x = f_1 (x,\dot x)   — (1)

\ddot x = f_2 (x, \dot x) + u

y=Cx — (2) if it is nonlinear, you need to know Lie Derivatives.

To achieve the first idea, we need to define a surface like the below. (Let’s assume that y is a scalar and differentiable.) 

s(x)=a_0 e + a_1 \dot{e}  where  e=y-y_d — (3)

Here we need to select a_0 and a_1 to make y->0 as t-> inf,s=0. You will see the reason after more several lines.

Let’s take the derivative of s(x) w.r.t. x. Then we can get the below equation.

\dot{s}(x)=a_0 \dot e + a_1 \ddot e  — (4)

In addition, if we add the additional term -\eta \text{sign}(s) we can obtain the below equation,

\dot{s}(x)=a_0 \dot e + a_1 \ddot e=-\eta \text{sign}(s) — (5)

Through a simple Lyapunov theorem, we can easily prove s-> 0 at t->inf .

if s=0, e-> 0 and \dot(e)-> 0, because we selected a_0 and a_1 to be like this.

Let’s look at (5),

a_0 \frac{d}{dt}(y-y_d)+a_1 \frac{d^2}{dt^2}(y-y_d)=-\eta \text{sign}(s)

=> C(a_0 \dot x +a_1 \ddot x)=-\eta \text{sign}(s)

=>C(a_0 f_1 +a_1 (f_2+u)))=-\eta \text{sign}(s)

=>u=((-\eta \text{sign} (s))/C - a_0 f_1)/a_1 -f_2

With this control input, we can control a nonlinear system with SMC.

it is difficult to explain quickly within short explanations…., if you need more explanation or questions, please leave me a reply.