• 제어로봇시스템학회논문지
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• 제20권8호
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• pp.875-881
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• 2014

The redundancy resolution of the seven DOF (Degree of Freedom) upper limb exoskeleton is key to the synchronous motion between a robot and a human user. According to the seven DOF human arm model, positioning and orientating the wrist can be completed by multiple arm configurations that results in the non-unique solution to the inverse kinematics. This paper presents analysis on the kinematic and dynamic aspect of the human arm movement and its effect on the redundancy resolution of the seven DOF human arm model. The redundancy of the arm is expressed mathematically by defining the swivel angle. The final form of swivel angle can be represented as a linear combination of two different swivel angles achieved by optimizing two cost functions based on kinematic and dynamic criteria. The kinematic criterion is to maximize the projection of the longest principal axis of the manipulability ellipsoid of the human arm on the vector connecting the wrist and the virtual target on the head region. The dynamic criterion is to minimize the mechanical work done in the joint space for each of two consecutive points along the task space trajectory. The contribution of each criterion on the redundancy was verified by the post processing of experimental data collected with a motion capture system. Results indicate that the bimodal redundancy resolution approach improved the accuracy of the predicted swivel angle. Statistical testing of the dynamic constraint contribution shows that under moderate speeds and no load, the dynamic component of the human arm is not dominant, and it is enough to resolve the redundancy without dynamic constraint for the realtime application.

• 제어로봇시스템학회논문지
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• 제19권7호
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• pp.653-660
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• 2013

This paper deals with the problem of unfairness among uplink TCP (Transmission Control Protocol) flows associated with frame aggregation employed in IEEE 802.11n WLANs (Wireless Local Area Networks). When multiple stations have uplink TCP flows and transmit TCP data packets to an AP (Access Point), the AP has to compete for channel access with stations for the transmission of TCP ACK (acknowledgement) packets to the stations. Due to this contention-based channel access, TCP ACKs tend to be accumulated in the AP's downlink buffer. We show that the frame aggregation in the MAC (Medium Access Control) layer increases TCP ACK losses in the AP and leads to the serious unfair operation of TCP congestion control. To resolve this problem, we propose the TAC (TCP ACK Compression) mechanism operating at the top of the AP's interface queue. By exploiting the properties of cumulative TCP ACK and frame aggregation, TAC serves only the representative TCP ACK without serving redundant TCP ACKs. Therefore, TAC reduces queue occupancy and prevents ACK losses due to buffer overflow, which significantly contributes to fairness among uplink TCP flows. Also, TAC enhances the channel efficiency by not transmitting unnecessary TCP ACKs. The simulation results show that TAC tightly assures fairness under various network conditions while increasing the aggregate throughput, compared to the existing schemes.

• Structural Monitoring and Maintenance
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• 제2권1호
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• pp.19-34
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• 2015

One of the main causes of a limited use of nondestructive evaluation (NDE) technologies in bridge deck assessment is the speed of data collection and analysis. The paper describes development and implementation of the RABIT (Robotics Assisted Bridge Inspection Tool) for data collection using multiple NDE technologies. The system is designed to characterize three most common deterioration types in concrete bridge decks: rebar corrosion, delamination, and concrete degradation. It implements four NDE technologies: electrical resistivity (ER), impact echo (IE), ground-penetrating radar (GPR), and ultrasonic surface waves (USW) method. The technologies are used in a complementary way to enhance the interpretation. In addition, the system utilizes advanced vision to complement traditional visual inspection. Finally, the RABIT collects data at a significantly higher speed than it is done using traditional NDE equipment. The robotic system is complemented by an advanced data interpretation. The associated platform for the enhanced interpretation of condition assessment in concrete bridge decks utilizes data integration, fusion, and deterioration and defect visualization. This paper concentrates on the validation and field implementation of two NDE technologies. The first one is IE used in the delamination detection and characterization, while the second one is the USW method used in the assessment of concrete quality. The validation of performance of the two methods was conducted on a 9 m long and 3.6 m wide fabricated bridge structure with numerous artificial defects embedded in the deck.

• 제어로봇시스템학회논문지
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• 제10권6호
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• pp.537-544
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• 2004

An effective methodology is reported for determining the optimal lot size of batch processing and storage networks which include uncertain demand forecasting. We assume that any given storage unit can store one material type which can be purchased from suppliers, internally produced, infernally consumed, transported to or from other sites and/or sold to customers. We further assume that a storage unit is connected to all processing and transportation stages that consume/produce or move the material to which that storage unit is dedicated. Each processing stage transforms a set of feedstock materials or intermediates into a set of products with constant conversion factors. A batch transportation process can transfer one material or multiple materials at once between sites. The objective for optimization is to minimize the probability averaged total cost composed of raw material procurement, processing setup, transportation setup and inventory holding costs as well as the capital costs of processing stages and storage units. A novel production and inventory analysis formulation, the PSW(Periodic Square Wave) model, provides useful expressions for the upper/lower bounds and average level of the storage inventory. The expressions for the Kuhn-Tucker conditions of the optimization problem can be reduced to two sub-problems. The first yields analytical solutions for determining lot sires while the second is a separable concave minimization network flow subproblem whose solution yields the average material flow rates through the networks for the given demand forecast scenario. The result of this study will contribute to the optimal design and operation of the global supply chain.

• 제어로봇시스템학회논문지
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• 제18권12호
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• pp.1106-1114
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• 2012

To achieve synchronized motion between a wearable robot and a human user, the redundancy must be resolved in the same manner by both systems. According to the seven DOF (Degrees of Freedom) human arm model composed of the shoulder, elbow, and wrist joints, positioning and orientating the wrist in space is a task requiring only six DOFs. Due to this redundancy, a given task can be completed by multiple arm configurations, and thus there exists no unique mathematical solution to the inverse kinematics. This paper presents analysis on the kinematic and dynamic aspect of the human arm movement and their effect on the redundancy resolution of the human arm based on a seven DOF manipulator model. The redundancy of the arm is expressed mathematically by defining the swivel angle. The final form of swivel angle can be represented as a linear combination of two different swivel angles achieved by optimizing different cost functions based on kinematic and dynamic criteria. The kinematic criterion is to maximize the projection of the longest principal axis of the manipulability ellipsoid for the human arm on the vector connecting the wrist and the virtual target on the head region. The dynamic criterion is to minimize the mechanical work done in the joint space for each two consecutive points along the task space trajectory. As a first step, the redundancy based on the kinematic criterion will be thoroughly studied based on the motion capture data analysis. Experimental results indicate that by using the proposed redundancy resolution criterion in the kinematic level, error between the predicted and the actual swivel angle acquired from the motor control system is less than five degrees.

• 제어로봇시스템학회논문지
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• 제20권1호
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• pp.70-77
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• 2014

This paper proposes a novel localization algorithm based on ego-motion which used Lucas-Kanade Optical Flow and warping image obtained through fish-eye lenses mounted on the robots. The omnidirectional image sensor is a desirable sensor for real-time view-based recognition of a robot because the all information around the robot can be obtained simultaneously. The preprocessing (distortion correction, image merge, etc.) of the omnidirectional image which obtained by camera using reflect in mirror or by connection of multiple camera images is essential because it is difficult to obtain information from the original image. The core of the proposed algorithm may be summarized as follows: First, we capture instantaneous $360^{\circ}$ panoramic images around a robot through fish-eye lenses which are mounted in the bottom direction. Second, we extract motion vectors using Lucas-Kanade Optical Flow in preprocessed image. Third, we estimate the robot position and angle using ego-motion method which used direction of vector and vanishing point obtained by RANSAC. We confirmed the reliability of localization algorithm using ego-motion based on fisheye warping image through comparison between results (position and angle) of the experiment obtained using the proposed algorithm and results of the experiment measured from Global Vision Localization System.

• 제어로봇시스템학회논문지
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• 제22권10호
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• pp.833-838
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• 2016

This paper presents a performance evaluation of various feature-initialization algorithms for forward-viewing mono-camera based simultaneous localization and mapping (SLAM), specifically in indoor environments. For mono-camera based SLAM, the position of feature points cannot be known from a single view; therefore, it should be estimated from a feature initialization method using multiple viewpoint measurements. The accuracy of the feature initialization method directly affects the accuracy of the SLAM system. In this study, four different feature initialization algorithms are evaluated in simulations, including linear triangulation; depth parameterized, linear triangulation; weighted nearest point triangulation; and particle filter based depth estimation algorithms. In the simulation, the virtual feature positions are estimated when the virtual robot, containing a virtual forward-viewing mono-camera, moves forward. The results show that the linear triangulation method provides the best results in terms of feature-position estimation accuracy and computational speed.

• 제어로봇시스템학회논문지
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• 제15권5호
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• pp.457-464
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• 2009

The weak classifier of AdaBoost algorithm is a central classification element that uses a single criterion separating positive and negative learning candidates. Finding the best criterion to separate two feature distributions influences learning capacity of the algorithm. A common way to classify the distributions is to use the mean value of the features. However, positive and negative distributions of Haar-like feature as an image descriptor are hard to classify by a single threshold. The poor classification ability of the single threshold also increases the number of boosting operations, and finally results in a poor classifier. This paper proposes a weak classifier that uses multiple criterions by adding a probabilistic criterion of the positive candidate distribution with the conventional mean classifier: the positive distribution has low variation and the values are closer to the mean while the negative distribution has large variation and values are widely spread. The difference in the variance for the positive and negative distributions is used as an additional criterion. In the learning procedure, we use a new classifier that provides a better classifier between them by selective switching between the mean and standard deviation. We call this new type of combined classifier the "Mixed Weak Classifier". The proposed weak classifier is more robust than the mean classifier alone and decreases the number of boosting operations to be converged.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2004년도 ICCAS
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• pp.582-587
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• 2004

Ultrasonic sensors are widely used in mobile robot applications to recognize external environments, because they are cheap, easy to use, and robust under varying lighting conditions. However, the recognition of objects using a ultrasonic sensor is not so easy due to its characteristics such as narrow beam width and no reflected signal from a inclined object. As one of the alternatives to resolve these problems, use of multiple sensors has been studied. A sequential driving system needs a long measurement time and does not take advantage of multiple sensors. Simultaneous and pulse coding driving system of ultrasonic sensor array cannot measure short distance as the length of the code becomes long. This problem can be resolved by multi-frequency driving of ultrasonic sensors, which allows multi-sensors to be fired simultaneously and adjacent objects to be distinguished. Accordingly, this paper presents a simultaneous and multi-frequency driving system for an ultrasonic sensor array for object recognition. The proposed system is designed and implemented using a DSP and FPGA. A micro-controller board is made using a DSP, Polaroid 6500 ranging modules are modified for firing the multi-frequency signals, and a 5-channel frequency modulated signal generating board is made using a FPGA. To verify the proposed method, experiments were conducted in an environment with overlapping signals, and the flight distances for each sensor were obtained from filtering of the received overlapping signals and calculation of the time-of-flights.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2003년도 ICCAS
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• pp.1927-1932
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• 2003

In a previous study, the authors have proposed the Cooperative Collision Avoidance (CCA) method which enables mobile robots to cooperatively avoid collisions, by extending the concept of the Velocity Obstacle to multiple robot systems. The method introduced an evaluation function considering an operation objective so that each robot can choose the velocity which optimizes the function. As the evaluation function could be of an arbitrary type, this method is applicable to a wide variety of tasks. However, it complicates the optimization of the function especially in real-time. In addition, construction of the evaluation function requires an operation objective of the other robot which is very hard to obtain without communication. In this paper, the CCA method is improved considering such problems for implementation. To decrease computational costs, the previous method is simplified by introducing two essential assumptions. Then, by treating the desired direction of locomotion for each robot as the operation objective, an operation objective estimator which estimates the desired direction of the other robot is introduced. The only measurement required is the other robot's relative position, since the other information can be obtained through the estimation. Hence, communicational devices that are necessary for most other cooperative methods are not required. Moreover, mobile robots employing the method can avoid collisions with uncooperative robots or moving obstacles as well as with cooperative robots. Consequently, this improved method can be applied to general dynamic environments consisting of various mobile robots.