• Journal of the Korean Society of Industry Convergence
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• v.21 no.3
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• pp.125-131
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• 2018

In this paper, we propose a homogeneous multisensor-based navigation algorithm for a mobile robot, which is intelligently searching the goal location in unknown dynamic environments with moving obstacles using multi-ultrasonic sensor. Instead of using "sensor fusion" method which generates the trajectory of a robot based upon the environment model and sensory data, "command fusion" method by fuzzy inference is used to govern the robot motions. The major factors for robot navigation are represented as a cost function. Using the data of the robot states and the environment, the weight value of each factor using fuzzy inference is determined for an optimal trajectory in dynamic environments. For the evaluation of the proposed algorithm, we performed simulations in PC as well as real experiments with mobile robot, AmigoBot. The results show that the proposed algorithm is apt to identify obstacles in unknown environments to guide the robot to the goal location safely.

• Journal of Astronomy and Space Sciences
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• v.20 no.4
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• pp.359-364
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• 2003

In this paper, we present multi-sensor data fusion for telematics application. Successful telematics can be realized through the integration of navigation and spatial information. The well-determined acquisition of vehicle's position plays a vital role in application service. The development of GPS is used to provide the navigation data, but the performance is limited in areas where poor satellite visibility environment exists. Hence, multi-sensor fusion including IMU (Inertial Measurement Unit), GPS(Global Positioning System), and DMI (Distance Measurement Indicator) is required to provide the vehicle's position to service provider and driver behind the wheel. The multi-sensor fusion is implemented via algorithm based on Kalman filtering technique. Navigation accuracy can be enhanced using this filtering approach. For the verification of fusion approach, land vehicle test was performed and the results were discussed. Results showed that the horizontal position errors were suppressed around 1 meter level accuracy under simulated non-GPS availability environment. Under normal GPS environment, the horizontal position errors were under 40㎝ in curve trajectory and 27㎝ in linear trajectory, which are definitely depending on vehicular dynamics.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.16 no.6
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• pp.1-15
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• 2017

Recently terrorism targets unspecified masses and causes massive destruction, which is so-called Super Terrorism. Many countries have tried hard to protect their citizens with various preparation and safety net. With inexpensive and advanced technologies of sensors, the surveillance systems have been paid attention, but few studies associated with the classification of the pedestrians' trajectories and the difference among themselves have attempted. Therefore, we collected individual trajectories at Samseoung Station using an analytical solution (system) of pedestrian trajectory by LiDAR sensor. Based on the collected trajectory data, a comprehensive framework of classifying the types of pedestrians' trajectories has been developed with data normalization and "trajectory association rule-based algorithm." As a result, trajectories with low similarity within the very same cluster is possibly detected.

• Journal of KIISE:Computing Practices and Letters
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• v.15 no.3
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• pp.145-153
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• 2009

Trajectory data are ubiquitous in the real world. Recent progress on satellite, sensor, RFID, video, and wireless technologies has made it possible to systematically track object movements and collect huge amounts of trajectory data. Accordingly, there is an ever-increasing interest in performing data analysis over trajectory data. In this paper, we develop a data mining tool for massive trajectory data. This mining tool supports three operations, clustering, classification, and outlier detection, which are the most widely used ones. Trajectory clustering discovers common movement patterns, trajectory classification predicts the class labels of moving objects based on their trajectories, and trajectory outlier detection finds trajectories that are grossly different from or inconsistent with the remaining set of trajectories. The primary advantage of the mining tool is to take advantage of the information of partial trajectories in the process of data mining. The effectiveness of the mining tool is shown using various real trajectory data sets. We believe that we have provided practical software for trajectory data mining which can be used in many real applications.

• Journal of the Korea Institute of Military Science and Technology
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• v.15 no.3
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• pp.250-256
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• 2012

Unmanned ground vehicles may be operated by remote control unit through the wireless communication or autonomously. However, the autonomous technology is still challenging and not perfectly developed. For some reason or other, the wireless communication is not always available. If wireless communication is abruptly disconnected, the UGV will be nothing but a lump of junk. What was worse, the UGV can be captured by enemy. This paper suggests a method, autonomous return technology with which the UGV can autonomously go back to a safer position along the reverse path. The suggested autonomous return technology for UGV is based on multi-correlated information based DB creation and matching. While SUGV moves by remote-control, the multi-correlated information based DB is created with the multi-sensor information; the absolute position of the trajectory is stored in DB if GPS is available and the hybrid MAP based on the fusion of VISION and LADAR is stored with the corresponding relative position if GPS is unavailable. In multi-correlated information based autonomous return, SUGV returns autonomously based on DB; SUGV returns along the trajectory based on GPS-based absolute position if GPS is available. Otherwise, the current position of SUGV is first estimated by the relative position using multi-sensor fusion followed by the matching between the query and DB. Then, the return path is created in MAP and SUGV returns automatically based on the MAP. Experimental results on the pre-built trajectory show the possibility of the successful autonomous return.

• Journal of Power System Engineering
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• v.10 no.3
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• pp.97-103
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• 2006

In this study, a position trajectory tracking control algorithm is proposed for a pneumatic artificial muscle driving apparatus composed of a actuator which imitates the muscle of human, a position sensor and a control valve. The controller applied to the driving apparatus is composed of a state feedback controller and disturbance observer. The feedback controller which feeds back position, velocity and acceleration is derived from the linear model of pneumatic artificial muscle driving apparatus. The disturbance observer is designed to improve trajectory tracking performance and to reduce the effect of model discrepancy. The effectiveness of the designed controller is proved by experiments and the experimental results show that the pneumatic artificial muscle driving apparatus with the proposed control algorithm tracks given position reference inputs accurately.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.7
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• pp.3150-3170
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• 2016

In Intelligent Transportation Systems (ITS), logistics distribution and mobile e-commerce, the real-time, accurate and reliable vehicle trajectory prediction has significant application value. Vehicle trajectory prediction can not only provide accurate location-based services, but also can monitor and predict traffic situation in advance, and then further recommend the optimal route for users. In this paper, firstly, we mine the double layers of hidden states of vehicle historical trajectories, and then determine the parameters of HMM (hidden Markov model) by historical data. Secondly, we adopt Viterbi algorithm to seek the double layers hidden states sequences corresponding to the just driven trajectory. Finally, we propose a new algorithm (DHMTP) for vehicle trajectory prediction based on the hidden Markov model of double layers hidden states, and predict the nearest neighbor unit of location information of the next k stages. The experimental results demonstrate that the prediction accuracy of the proposed algorithm is increased by 18.3% compared with TPMO algorithm and increased by 23.1% compared with Naive algorithm in aspect of predicting the next k phases' trajectories, especially when traffic flow is greater, such as this time from weekday morning to evening. Moreover, the time performance of DHMTP algorithm is also clearly improved compared with TPMO algorithm.

• Transactions of the Korean Society of Automotive Engineers
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• v.12 no.6
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• pp.182-190
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• 2004

This paper presents reconstruction analysis of vehicle trajectory using records of a developed black box, and results of validation tests. For reconstruction of vehicle trajectory, the black box records the longitudinal and lateral accelerations and yaw-rate of vehicle during a pre-defined time period before and after the accident. One 2-axis accelerometer is used for measuring accelerations, and one vibrating structure type gyroscope is used for measuring yaw-rate of vehicle. The vehicle's planar trajectory can be reconstructed by integrating twice accelerations along longitudinal and lateral directions with yaw-rate values. However, there may be many kinds of errors in sensor measurements. The causes of errors are as follows: mis-alignment, low frequency offset drift, high frequency noise, and projecting 3-dimensional motion into 2-dimensional motion. Therefore, some procedures are taken for error compensation. In order to evaluate the reliability and the accuracy of trajectory reconstruction results, the black box was mounted on a passenger car. The vehicle was driven and tested along various specified lanes. Through the tests, the accuracy and usefulness of the reconstruction analysis have been validated.

• The Journal of Korea Robotics Society
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• v.18 no.4
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• pp.496-504
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• 2023

This paper recognizes the motion intention of the wearer using a muscle stiffness sensor and proposes a control system for a wearable robot based on this. The proposed system recognizes the onset time of the motion using sensor data, determines the assistance mode, and provides assistive torque to the hip flexion/extension motion of the wearer through the generated reference trajectory according to the determined mode. The onset time of motion was detected using the CUSUM algorithm from the muscle stiffness sensor, and by comparing the detection results of the onset time with the EMG sensor and IMU, it verified its applicability as an input device for recognizing the intention of the wearer before motion. In addition, the stability of the proposed method was confirmed by comparing the results detected according to the walking speed of two subjects (1 male and 1 female). Based on these results, the assistance mode (gait assistance mode and muscle strengthening mode) was determined based on the detection results of onset time, and a reference trajectory was generated through cubic spline interpolation according to the determined assistance mode. And, the practicality of the proposed system was also confirmed by applying it to an actual wearable robot.

• Smart Structures and Systems
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• v.7 no.2
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• pp.103-116
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• 2011

This paper is concerned with the trajectory tracking and vibration suppression of a single-link flexible arm by using piezoelectric materials. The dynamics of a single flexible arm with PZT patches as sensor and actuator is derived using extended Hamilton's principle. Resulting equations show that the coupled beam dynamics including beam vibration and its rigid in-plane rotation takes place in two different time scales. By using singular perturbation theory, the system dynamics is divided into two subsystems. Then, a composite control scheme is elaborated that makes the orientation of the arm track a desired trajectory while suppressing its vibration. The proposed controller has two parts: one is a tracking controller designed for the slow (rigid) subsystem, and the other one is a stabilizing controller for the fast (flexible) subsystem. The outputs considered for the system are angular position of the hub and voltage of the sensor mounted on the structure. To avoid requiring further measurements of beam vibration and also angular velocity of the hub for the fast and slow control laws, respectively, two sliding mode observers for estimating the unknown states are also designed.