• Journal of Institute of Control, Robotics and Systems
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• v.18 no.4
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• pp.328-336
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• 2012

In this paper, we propose a robust fingertip extraction and extended Continuously Adaptive Mean Shift (CAMSHIFT) based robust hand gesture recognition for natural human-like HRI (Human-Robot Interaction). Firstly, for efficient and rapid hand detection, the hand candidate regions are segmented by the combination with robust $YC_bC_r$ skin color model and haar-like features based adaboost. Using the extracted hand candidate regions, we estimate the palm region and fingertip position from distance transformation based voting and geometrical feature of hands. From the hand orientation and palm center position, we find the optimal fingertip position and its orientation. Then using extended CAMSHIFT, we reliably track the 2D hand gesture trajectory with extracted fingertip. Finally, we applied the conditional density propagation (CONDENSATION) to recognize the pre-defined temporal motion trajectories. Experimental results show that the proposed algorithm not only rapidly extracts the hand region with accurately extracted fingertip and its angle but also robustly tracks the hand under different illumination, size and rotation conditions. Using these results, we successfully recognize the multiple hand gestures.

• Journal of Institute of Control, Robotics and Systems
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• v.15 no.4
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• pp.451-455
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• 2009

This paper provides a comparison of global path planning method in single string by using pulled and pushed SOFM (Self-Organizing Feature Map) which is a method among a number of neural network. The self-organizing feature map uses a randomized small valued initial-weight-vectors, selects the neuron whose weight vector best matches input as the winning neuron, and trains the weight vectors such that neurons within the activity bubble are move toward the input vector. On the other hand, the modified SOFM method in this research uses a predetermined initial weight vectors of the one dimensional string, gives the systematic input vector whose position best matches obstacles, and trains the weight vectors such that neurons within the activity bubble are move toward or reverse the input vector, by rising a pulled- or a pushed-SOFM. According to simulation results one can conclude that the modified neural networks in single string are useful tool for the global path planning problem of a mobile robot. In comparison of the number of iteration for converging to the solution the pushed-SOFM is more useful than the pulled-SOFM in global path planning for mobile robot.

• Journal of Institute of Control, Robotics and Systems
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• v.15 no.10
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• pp.1029-1038
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• 2009

This paper proposes a gain switching algorithm for joint position control of a hydraulic humanoid robot. Accurate position control of the lower body is one of the basic requirements for robust balance and walking control. Joint position control is more difficult for hydraulic robots than it is for electric robots because of an absence of reduction gear and better back-drivability of hydraulic joints. Backdrivability causes external forces and torques to have a large effect on the position of the joints. External ground reaction forces therefore prevent a simple proportional-derivative (PD) controller from realizing accurate and fast joint position control. We propose a state feedback controller for joint position control of the lower body, define three modes of state feedback gains, and switch the gains according to the Zero Moment Point (ZMP) and linear interpolation. Dynamic equations of hydraulic actuators were experimentally derived and applied to a robot simulator. Finally, the performance of the algorithm is evaluated with dynamic simulations.

• Journal of Institute of Control, Robotics and Systems
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• v.14 no.11
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• pp.1146-1154
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• 2008

This paper presents a method for the acceleration analysis of multi-legged walking robots in consideration of the frictional ground contact. This method is based on both unified dynamic equation for finding the acceleration of a robot's body and constraint equation for satisfying no-slip condition. After the dynamic equation representing relationship between actuator torques and body acceleration, is derived from the force and acceleration relationship between foot and body's gravity center, the constraint equation is formulated to reconfigure the maximum torque boundaries satisfying no-slip condition from given original actuator torque boundaries. From application of the reconfigured torques to the dynamic equation, interested acceleration boundaries are obtained. The approach based on above two equations, is adapted to the changes of degree-of-freedoms of legs as well as friction of ground. And the method provides the maximum translational and rotational acceleration boundaries of body's center that are achievable in every direction without occurring slipping at the contact points or saturating all actuators. Given the torque limits in infinite normsense, the resultant accelerations are derived as a polytope. From the proposed method, we obtained achievable acceleration boundaries of 4-legged and 6-legged walking robot system successfully.

• Journal of Institute of Control, Robotics and Systems
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• v.20 no.11
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• pp.1153-1163
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• 2014

An efficient 3D SLAM (Simultaneous Localization and Map Building) method is developed for urban building environments using a tilted 2D LRF (Laser Range Finder), in which a 3D map is composed of perpendicular/horizontal planar polygons. While the mobile robot is moving, from the LRF scan distance data in each scan period, line segments on the scan plane are successively extracted. We propose an "expected line segment" concept for matching: to add each of these scan line segments to the most suitable line segment group for each perpendicular/horizontal planar polygon in the 3D map. After performing 2D localization to determine the pose of the mobile robot, we construct updated perpendicular/horizontal infinite planes and then determine their boundaries to obtain the perpendicular/horizontal planar polygons which constitute our 3D map. Finally, the proposed SLAM algorithm is validated via extensive simulations and experiments.

• Journal of the Korean Institute of Intelligent Systems
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• v.14 no.2
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• pp.171-177
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• 2004

In this paper, we propose a cognition-based navigational algorithm for mobile robots in dynamic environments. The proposed algorithm consists of two main stages: (i) the fuzzy logic-based perception stage that constructs knowledge from the sensory data for subsequent usage in reasoning, and (ii) the planning stage that identifies the path between a starting and a goal position within its environment on the basis of the knowledge base on the environment and information from the perception stage. A mobile robot reasons places and moves to goal using ambiguous information and ambiguous knowledge through ‘perception’ and ‘planning’. We provide computer simulation results for a mobile robot in order to show the validity of the proposed algorithm.

• Journal of the Korean Institute of Intelligent Systems
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• v.14 no.4
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• pp.396-401
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• 2004

Recently a framework for the cognition-based navigational planning of a mobile robot on dynamic environment has been proposed, and simulation results applied it to the static environment been presented [1]. In this paper, we propose a linguistic map-based framework for the navigational planning of mobile robots, which is applicable to the dynamic environment including not only static obstacles but also dynamic obstacles such as temporal-spatio obstacles, by extending Lee et al. 's framework, and provide computer simulation results obtained by applying to a mobile robot on the dynamic environment in order to show the validity of the proposed algorithm.

• Journal of the Korean Institute of Intelligent Systems
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• v.13 no.2
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• pp.180-185
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• 2003

This paper proposes an intelligent navigation algorithm for multiple mobile robots under unknown dynamic environment. The proposed algorithm consists of three basic parts as follows. The first part based on the fuzzy rule generates the turning angle and moving distance of the robot for goal approach without obstacles. In the second part, using both fuzzy and neural network, the angle and distance of the robot to avoid collision with dynamic and static obstacles are obtained. The final adjustment of the weighting factor based on fuzzy rule for moving and avoiding distance of the robots is provided in the third stage. The experiments which demonstrate the performance of the proposed intelligent controller is described.

• Journal of the Korean Institute of Intelligent Systems
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• v.22 no.1
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• pp.22-27
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• 2012

This paper presents a ball screw-driven robotic gripper mechanism which is possible to grasp an object and analyzes its kinematic feature for grasping by simulation. For the purpose of identifying the feature of the robot gripper, we try to confirm the kinematics relating the joint space of the driving actuator to the gripper's tip space. To be specific, the proposed robot gripper employs one actuator and a symmetrical closed-chain structure. As a result, the specified robot gripper has an advantage of robustness to external forces structurally, and it is easy to implement simple grasping operations. Also the gripper has a useful squeezing effect for power grasping.

• Journal of the Korean Institute of Intelligent Systems
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• v.17 no.1
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• pp.19-25
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• 2007

Among the precision reduction gent drivers lot robot system, the cycloid reducer is well known for it's high performances. Designing this reducer, there are many factors which must be considered. First, a geometrical analysis of tooth shape must be drawn from the basic concept. Second, loads, stresses and modification factors on tooth should be calculated exactly. Finally, a computer software to optimize the design of cycloid tooth needs developing on the basis of the geometric and force equations. In this research, the expert system to design the cycloid reducer was developed using Visual C++ so, the most important factors can be obtained automatically as the user put the simple input data.