• Journal of the Korean Society of Industry Convergence
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• v.18 no.3
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• pp.167-172
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• 2015

In this paper, we propose a new approach to design and control a smart gripper of robot system. A control method for flexible grasping a object in partially unknown environment was proposed, where a proximate sensor detecting the distance between the fingertip and object was used. Based on the proximate sensor signal the finger motion controller could plan the grasping process divided in three phases. The first step is scanning process which two first joints were moved to mid-position of the detected range by a state-variable feedback position controller, after the scanning was finished. The contact force of fingertip was then controlled using the detection sensor of the servo controller for finger joint control. The proposed grasping planning was tested on rectangular bar.

• International Journal of Computer Science & Network Security
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• v.24 no.1
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• pp.17-22
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• 2024

The objective of this work is to design a low cost yet fully functional 4-DOF articulate manipulator for educational applications. The design is based on general purpose, programmable smart servo motors namely the Dynamixel Ax-12. The mechanism for motion was developed by formulating the equations of kinematics and subsequent solutions for joint space variables. The trajectory of end-effector in joint variable space was determined by interpolation of a 3rd order polynomial. The solutions were verified through computer simulations and ultimately implemented on the hardware. Owing to the feedback from the built-in sensors, it is possible to correct the positioning error due to loading effects. The proposed solution offers an efficient and cost-effective platform to study the trajectory planning as well as dynamics of the manipulator.

• Journal of the Korea Society of Computer and Information
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• v.26 no.3
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• pp.9-17
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• 2021

In this paper, an autonomous multi-agent path planning using reinforcement learning for monitoring of infrastructures and resources in a computationally distributed system was proposed. Reinforcement-learning-based multi-agent exploratory system in a distributed node enable to evaluate a cumulative reward every action and to provide the optimized knowledge for next available action repeatedly by learning process according to a learning policy. Here, the proposed methods were presented by (a) approach of dynamics-based motion constraints multi-agent path-planning to reduce smaller agent steps toward the given destination(goal), where these agents are able to geographically explore on the environment with initial random-trials versus optimal-trials, (b) approach using agent sub-goal selection to provide more efficient agent exploration(path-planning) to reach the final destination(goal), and (c) approach of reinforcement learning schemes by using the proposed autonomous and asynchronous triggering of agent exploratory phases.

• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.2
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• pp.220-226
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• 2014

This paper proposes an enhanced fast scanning method for multi-agent robot system. Passive RFID tag can read and store the information within the range of recognizable RF tag reader. Based on this information of Passive RFID tag, the position of mobile robot can be estimated and at the same time, the efficiency of scanning process can be improved because it provides a scanning trace for other mobile robots. This paper proposes an dfficient motion planning algorithm for mobile robots in a smart floor environment.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.2045-2050
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• 2003

This paper describes the development of service robotic systems with the Tripodal schematic control architecture. We show practical advantages of the proposed architecture by giving examples of our experience. First, we explain how to add new task using Tripodal architecture approach. The Tripodal architecture provides some crucial organizing principles and core components that are used to build the basis for the system. Thus, the newly developed behaviors, motion algorithm, knowledge, and planning schemes are arranged so as to guarantee the efficiency of the performance of components. Second, we describe the reusability and scaleability of our architecture by introducing the implementation process of the guide robot Jinny. Most of modules developed for former robots like PSR-1 and PSR-2 systems are used directly to the Jinny system without significant modification. Experimental results clearly showed that the developed strategy is useful, even if the hardware configurations as well as software algorithms are more complex and more accumulating.

• 제어로봇시스템학회:학술대회논문집
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• 1998.10a
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• pp.14-19
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• 1998

In this paper, a target approachable force-guided control with adaptive accommodation for the complex assembly is presented. The complex assembly (CA) is defined as a task which deals with complex shaped parts including concavity or whose environment is so complex that unexpected contacts occur frequently during insertion. CA tasks are encountered frequently in the field of the manufacturing automation and various robot applications. To make CA successful, both the bounded wrench condition and the target approachability condition should be satisfied simultaneously during insertion. By applying the convex optimization technique, an optimum target approaching twist can be determined at each instantaneous contact state as a global minimum solution. Incorporated with an admissible perturbation method, a new CA algorithm using only the sensed resultant wrench and the target twist is developed without motion planning nor contact analysis which requires the geometry of the part and the environment. Finally, a VME-bus based real-time control system is built to experiment various CA task. T-insertion task as a planar CA and double-peg assembly task as a spacial assembly were successfully executed by implementing the new force-guided control with adaptive accommodation.

• The Journal of Korea Robotics Society
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• v.17 no.4
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• pp.417-424
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• 2022

In this paper, the developed trunk cargo unloading automation system is introduced, and the RGB-D sensor-based box loading situation recognition method and unloading plan applied to this system are suggested. First of all, it is necessary to recognize the position of the box in a truck. To do this, we first apply CNN-based YOLO, which can recognize objects in RGB images in real-time. Then, the normal vector of the center of the box is obtained using the depth image to reduce misrecognition in parts other than the box, and the inner wall of the truck in an image is removed. And a method of classifying the layers of the boxes according to the distance using the recognized depth information of the boxes is suggested. Given the coordinates of the boxes on the nearest layer, a method of generating the optimal path to take out the boxes the fastest using this information is introduced. In addition, kinematic analysis is performed to move the conveyor to the position of the box to be taken out of the truck, and kinematic analysis is also performed to control the robot arm that takes out the boxes. Finally, the effectiveness of the developed system and algorithm through a test bed is proved.