• Journal of Institute of Control, Robotics and Systems
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• v.17 no.7
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• pp.659-667
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• 2011

The study of bipedal robot is towards similar shape and function with human. In this paper, we propose a human-like walking pattern compatible to the flexible foot with toe and heel structure. The new walking pattern for a bipedal robot consists of ZMP, center of mass (CoM), and ankle trajectory and is drawn by considering human kinesiology. First, the ZMP trajectory moves forward without stopping at a point even in the single support phase. The corresponding CoM trajectory to the ZMP one is derived by solving differential equations. As well, a CoM trajectory for the vertical axis is added by following the idea of human motion. The ankle trajectory closely mimics the rotational motion of human ankles during taking off and landing on the ground. The advantages of the proposed walking pattern are demonstrated by showing improved stability, decreased ankle torque, and the longer step length capability. Specifically, it is interesting to know that the vertical CoM motion is able to compensate for the initial transient response.

• Journal of Biomedical Engineering Research
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• v.15 no.4
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• pp.455-464
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• 1994

The intelligent trajectory control method that controls moving direction and average velocity for a prosthetic arm is proposed by pattern recognition and force estimations using EMG signals. Also, we propose the real time trajectory planning method which generates continuous accelleration paths using 3 stage linear filters to minimize the impact to human body induced by arm motions and to reduce the muscle fatigue. We use combination of MLP and fuzzy filter for pattern recognition to estimate the direction of a muscle and Hogan's method for the force estimation. EMG signals are acquired by using a amputation simulator and 2 dimensional joystick motion. The simulation results of proposed prosthetic arm control system using the EMG signals show that the arm is effectively followed the desired trajectory depended on estimated force and direction of muscle movements.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.65 no.9
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• pp.1551-1556
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• 2016

A combined CPG (Central Pattern Generator) based foot trajectory and GP (Genetic Programming) based joint compensation method is presented for the adaptive humanoid walking. The CPG based foot trajectory methods have been successfully applied to basic slops and variable slops with slow rates, but have a limitation for the steep slop terrains. In order to increase an adaptability of humanoid walking for the rough terrains, a GP based joint compensation method is proposed and combined to the CPG (Central Pattern Generator) based foot trajectory method. The experiments using humanoid robot Nao are conducted in an ODE based Webots simulation environmemt to verify a stability of walking for the various aslope terrains. The proposed method is compared to the previous CPG foot trajectory technique and shows better performances especially for the steep varied slopes.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2022.11a
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• pp.147-148
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• 2022

Recently, as a number of accidents occur while berthing ships, the need for safety measures for ship operation in ports is emphasized. In order to quantitatively analyze the contents of safety measures in Busan New Port, this study collected ship trajectory data,, and based on this data, applied a maritime artificial intelligence algorithm to analyze the trajectory pattern. As a result, the waypoint of the ship arriving and departing Busan New Port was derived and the operation pattern of the ship's speed and course was proposed.

• Journal of the Korea Society of Computer and Information
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• v.26 no.12
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• pp.133-142
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• 2021

Travel pattern recognition is widely used in many aspects such as user trajectory query, user behavior prediction, interest recommendation based on user location, user privacy protection and municipal transportation planning. Because the current recognition accuracy cannot meet the application requirements, the study of travel pattern recognition is the focus of trajectory data research. With the popularization of GPS navigation technology and intelligent mobile devices, a large amount of user mobile data information can be obtained from it, and many meaningful researches can be carried out based on this information. In the current travel pattern research method, the feature extraction of trajectory is limited to the basic attributes of trajectory (speed, angle, acceleration, etc.). In this paper, permutation entropy was used as an eigenvalue of trajectory to participate in the research of trajectory classification, and also used as an attribute to measure the complexity of time series. Velocity permutation entropy and angle permutation entropy were used as characteristics of trajectory to participate in the classification of travel patterns, and the accuracy of attribute classification based on permutation entropy used in this paper reached 81.47%.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.38B no.11
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• pp.934-936
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• 2013

A routing scheme for a group of mobile users for wireless ad-hoc networks is presented. The proposed scheme mines social activity patterns from wireless traces, and exploits social user group for efficient data routing among users based on a data publish approach. Simulation results based on real-world wireless traces show that our routing scheme reduces routing cost for a large mobile user group with a factor of 1.8 compared to a baseline counterpart.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.435-437
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• 2003

This study presents work to utilize RADARSAT SAR image for forecast oil slick trajectory movement. The fractal dimension algorithm used to detect oil slick. The Doppler frequency shift and quasi-linear model was used to simulate a current pattern from RADARSAT image. The Fay’s algorithm of oil slick spreading was developed based on a Doppler frequency shift model. Thus, the study shows that fractal dimension algorithm discriminated the oil slick from the surrounding water features. The quasi-linear model shows that the current pattern can be simulated from single RADARSAT image. The oil slick trajectory model shows that after 48 hrs, the oil slick parcels deposited along the coastal waters.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.2492-2497
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• 2003

This paper presents optimal walking trajectory generation for a quadruped robot with genetic-fuzzy algorithm. In order to move a quadruped robot smoothly, both generations of optimal leg trajectory and free walking are required. Generally, making free walking is difficult to realize for a quadruped robot, because the patterned trajectory may interfere in the free walking. In this paper, we suggest the generation method for the leg trajectory satisfied with free walking pattern so as to avoid obstacle and walk smoothly. We generate via points of leg with respect to body motion, and then we use the genetic-fuzzy algorithm to search for the optimal via velocity and acceleration information of legs. All these methods are verified with PC simulation program, and implemented to SERO-V robot.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.67 no.5
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• pp.663-668
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• 2018

A combined CPG (Central Pattern Generator) based foot trajectory and GA (Genetic Algorithm) based joint compensation method is presented for adaptive humanoid walking. In order to increase an adaptability of humanoid walking for rough terrains, the experiment for rolling terrains are introduced. The CPG based foot trajectory method has been successfully applied to basic slops and variable slops, but has a limitation for the rolling terrains. The experiments are conducted in an ODE based Webots simulation environment using humanoid robot Nao to verify a stability of walking for various rolling terrains. The proposed method is compared to the previous CPG foot trajectory technique and shows better performance especially for the cascade rolling terrains.

• Journal of the Korean Society for Aviation and Aeronautics
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• v.22 no.2
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• pp.1-8
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• 2014

This paper introduces a new framework of predicting the arrival time of an aircraft by incorporating the probabilistic information of what type of trajectory pattern will be applied by human air traffic controllers. The proposed method is based on identifying the major patterns of vectored trajectories and finding the statistical relationship of those patterns with various traffic complexity factors. The proposed method is applied to the traffic scenarios in real operations to demonstrate its performances.