• Journal of Korean Society for Geospatial Information Science
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• v.25 no.2
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• pp.21-29
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• 2017

For the efficient and stable operation of autonomous vehicles or advanced driver assistance systems being actively studied nowadays, it is important to determine the positions of the vehicle accurately and economically. A satellite based navigation system is mainly used for positioning, but it has a limitation in signal blockage areas. To overcome this limitation, sensor fusion methods including additional sensors such as an inertial navigation system have been mainly proposed but the high sensor cost has been a problem. In this work, we develop a vehicle position estimation algorithm using in-vehicle sensors and a low-cost imaging sensor without any expensive additional sensor. We determine the vehicle positions using the velocity and yaw-rate of a car from the in-vehicle sensors and the position and attitude of the camera based on the single photo resection process. For the evaluation, we built a prototype system, acquired test data using the system, and estimated the trajectory. The proposed algorithm shows the accuracy of about 40% higher than an in-vehicle sensor only method.

• Journal of Institute of Control, Robotics and Systems
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• v.15 no.2
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• pp.148-154
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• 2009

In this paper, an embedded Kalman filter for a time-delayed controller is designed on an FPGA to estimate accelerations of the robot arm. When the time-delayed controller is used as a controller, the inertia estimation along with accelerations is needed to form the control law. Although the time-delayed controller is known to be robust to cancel out uncertainties in the nonlinear systems, performances are very much dependent upon estimating the acceleration term ${\ddot{q}}(t-{\lambda})$ along with inertia estimation ${\hat{D}}(t-{\lambda})$. Estimating accelerations using the finite difference method is quite simple, but the accuracy of estimation is poor specially when the robot moves slowly. To estimate accelerations more accurately, various filters such as the least square fit filter and the Kalman filter are introduced and implemented on an FPGA chip. Experimental studies of following the desired trajectory are conducted to show the performance of the controller. Performances of different filters are investigated experimentally and compared.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.28 no.8 s.227
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• pp.1075-1086
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• 2004

In this paper, Time Delay Control(TDC) for robot manipulators is analyzed and its problems are founded. In order to remedy the problems, the enhanced controller is proposed and analyzed. The effect of friction associated with TDC is reported and its cause is presented. Through the analysis, simulation and experiment, it is shown that the friction effect causes serious degradation in control performance and that it is a result of the error of Time Delay Estimation(TDE) in TDC. In order to remedy the problems, TDC combined with Internal Model Control(IMC) concept is proposed. The proposed compensator is effective enough to handle the bad effect of friction, and is so simple and efficient as to match positive attribute of TDC. The simulation and experimental results show the effectiveness of proposed controller against the friction of the robot manipulators.

• Journal of Electrical Engineering and Technology
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• v.12 no.3
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• pp.1271-1279
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• 2017

A highly accurate model-free controller is proposed for trajectory tracking control of robot manipulators. The proposed controller incorporates time-delay estimation (TDE) to estimate and cancel continuous nonlinearities of robot dynamics, and exploits fuzzy logic systems to suppress the effect of the TDE error, which is due to discontinuous nonlinearities such as friction. To this end, integral sliding mode is defined using desired error dynamics, and a Mamdani-type fuzzy inference system is constructed. As a result, the proposed controller achieves the desired error dynamics well. Implementation of the proposed controller is easy because the design of the controller is intuitive and straightforward, and calculations of the complex robot dynamics are not required. The tracking performance of the proposed controller is verified experimentally using a 3-degree of freedom PUMA-type robot manipulator.

• Journal of KIISE:Software and Applications
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• v.37 no.8
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• pp.641-646
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• 2010

This paper presents algorithms for real-time navigation of a humanoid robot with a stereo vision but no other sensors. Using the algorithms, a robot can recognize its 3D environment by retrieving SIFT features from images, estimate its position through the Kalman filter, and plan its path to reach a destination avoiding obstacles. Our approach focuses on estimating the robot’s central walking path trajectory rather than its actual walking motion by using an approximate model. This strategy makes it possible to apply mobile robot localization approaches to humanoid robot localization. Simple collision free path planning and motion control enable the autonomous robot navigation. Experimental results demonstrate the feasibility of our approach.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.35 no.5
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• pp.533-541
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• 2011

This paper presents a frequency-response test performed on an antagonistic actuation system consisting of two Mckibben pneumatic artificial muscles and a pneumatic circuit with pressure valves. Varying switching frequency to pressure valves from 0.1 Hz to 5 Hz, parameters of a linear model were estimated optimally to predict dynamic characteristics of the antagonistic actuation. A model-base control scheme with estimated parameters was built for the precise trajectory tracking of the antagonistic structure and realized on a reconfigurable embedded control system, CompactRIO. Experimental results showed that the proposed model-based control scheme gave good performance in trajectory tracking comparing with a PD control scheme when square wave and sinusoidal wave were given as references to follow.

• Journal of Institute of Control, Robotics and Systems
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• v.10 no.1
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• pp.47-53
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• 2004

An electro-hydraulic manipulator using hydraulic actuators has many nonlinear elements, and its parameter fluctuations are greater than those of an electrically driven manipulator. So it is quite difficult to obtain stable control performance. We have applied a disturbance estimation and compensation type robust control to all the axes in a 6-link electro-hydraulic manipulator. It was confirmed that the performance of trajectory tracking and attitude regulating was greatly improved by the disturbance observer. For autonomous assembly tasks, it is said that compliance control is one of the most popular methods in contact task. We have proposed a compliance control based on the position control by a disturbance observer for our manipulator system. To realize more stable contact work, the states in the compliance loop are feedbacked, where not only displacement but also the velocity and acceleration are considered. We have also applied this compliance control to the Peg-in-Hole insertion task and proposed new methods of (1)rotating of the end-effector periodically in order to reduce the friction force, (2)random searching for the center of a hole and (3)trajectory modification to reduce the impact force. As a result of these new methods, it could be experimentally confirmed that the Peg-in-Hole insertion task with a clearance of 0.007 [mm] could be achieved.

• Journal of Information Processing Systems
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• v.13 no.4
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• pp.951-969
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• 2017

In this paper, we newly propose a traffic information service model that collects traffic information sensed by an individual vehicle in real time by using a smart device, and which enables drivers to share traffic information on all roads in real time using an application installed on a smart device. In particular, when the driver requests traffic information for a specific area, the proposed driver-personalized service model provides him/her with traffic information on the driving directions in advance by predicting the driving directions of the vehicle based on the learning of the driving records of each driver. To do this, we propose a traffic information management model to process and manage in real time a large amount of online-generated traffic information and traffic information requests generated by each vehicle. We also propose a road node-based indexing technique to efficiently store and manage location-based traffic information provided by each vehicle. Finally, we propose a driving learning and prediction model based on the hidden Markov model to predict the driving directions of each driver based on the driver's driving records. We analyze the traffic information processing performance of the proposed model and the accuracy of the driving prediction model using traffic information collected from actual driving vehicles for the entire area of Seoul, as well as driving records and experimental data.

• International Journal of Internet, Broadcasting and Communication
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• v.15 no.3
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• pp.176-184
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• 2023

Points of interest (POIs) are widely used in tourism recommendations and to provide information about areas of interest. Currently, situation judgement using POI and GPS data is mainly rule-based. However, this approach has the limitation that inferences can only be made using predefined POI information. In this study, we propose an algorithm that uses POI data, GPS data, and schedule information to calculate the current speed, location, schedule matching, movement trajectory, and POI coverage, and uses machine learning to determine whether to stay or go. Based on the input data, the clustered information is labelled by k-means algorithm as unsupervised learning. This result is trained as the input vector of the SVM model to calculate the probability of moving and staying. Therefore, in this study, we implemented an algorithm that can adjust the schedule using the travel schedule, POI data, and GPS information. The results show that the algorithm does not rely on predefined information, but can make judgements using GPS data and POI data in real time, which is more flexible and reliable than traditional rule-based approaches. Therefore, this study can optimize tourism scheduling. Therefore, the stay detection algorithm using GPS movement trajectories and POIs developed in this study provides important information for tourism schedule planning and is expected to provide much value for tourism services.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2022.06a
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• pp.150-154
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• 2022

The Automatic Ship Identification System(AIS) is importantly used to evaluate the trajectory of ships and the possibility of collision between ships. However, it is difficult to provide real-time information due to the limitation of the transmission intervals. Most of the studies to improve this are conducted based on ideal data, so there is a problem that it is hard to respond to the actual situation. Therefore, in this paper, we propose a discrete Kalman filter-based method that reflects the turning angle according to the type of trajectory, to provide real-time position information on real-time data. In addition, the accuracy evaluation of the proposed algorithm is conducted through experiments using actual data.