• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.10a
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• pp.354-360
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• 1998

In general, it is very difficult to find optimal fuzzy rules by experience when a system is dynamical and/or complex. Futhermore proper fuzzy partitioning is not deterministic and there is no unique solution. Therefore we propose a new design method of an optimal fuzzy logic controller, that is a co-evolutionary genetic algorithm finding optimal fuzzy rule and proper membership functions at the same time. We formalize the relation between fuzzy rules and membership functions in terms of fitness. We review the typical approaching methods to co-evolutionary genetic algorithms , and then classify them by fitness relation matrix. Applications of the proposed method to a path planning problem of autonomous mobile robots when moving objects exist are presented to demonstrate the performance and effectiveness of the method.

• Proceedings of the KIEE Conference
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• 2003.07d
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• pp.2453-2455
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• 2003

We propose a path planning method to reduce the inspection time of AOI (automatic optical inspection) machines in SMT (surface mount technology) in-line system. Inspection windows of board should be clustered to consider the FOV (field-of-view) of camera. The number of clusters is desirable to be minimized in order to reduce the overall inspection time. We newly propose a genetic algorithm to minimize the number of clusters for a given board. Comparative simulation results are presented to verify the usefulness of proposed algorithm.

• Proceedings of the Korea Information Processing Society Conference
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• 2019.05a
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• pp.412-414
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• 2019

경로 계획은 에이전트가 로봇이 특정 목적지에 도착할 수 있도록 에이전트가 수집한 정보를 바탕으로 경로를 설정하는 작업을 뜻한다. 부분 관측만 가능한 맵인 경우 에이전트 이동마다 새로 수집되는 정보들을 바탕으로 마르코프 의사결정 과정을 사용한 가치 반복 알고리즘이 널리 사용되지만, 제안된 가치 반복 알고리즘 사용 시 매 행동마다 모든 공간의 최적 경로를 계산하기 때문에 시간이 오래 걸리는 문제점이 있다. 이에 본 논문에서는 에이전트가 한 번에 탐색하는 범위가 제한되어 있다는 점에 착안하여 탐색 반경 내에 속하는 공간의 가치 함수 값을 미리 추정하여 효율적으로 최적의 경로를 추정하는 가치 반복 알고리즘을 제안한다.

• Journal of Auto-vehicle Safety Association
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• v.13 no.4
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• pp.123-128
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• 2021

This paper present box feature estimation from LiDAR point cluster using maximum likelihood Method. Previous LiDAR tracking method for autonomous driving shows high accuracy about velocity and heading of point cluster. However, Assuming the average position of a point cluster as the vehicle position has a lower accuracy than ground truth. Therefore, the box feature estimation algorithm to improve position accuracy of autonomous driving perception consists of two procedures. Firstly, proposed algorithm calculates vehicle candidate position based on relative position of point cluster. Secondly, to reflect the features of the point cluster in estimation, the likelihood of the particle scattered around the candidate position is used. The proposed estimation method has been implemented in robot operating system (ROS) environment, and investigated via simulation and actual vehicle test. The test result show that proposed cluster position estimation enhances perception and path planning performance in autonomous driving.

• Journal of the Korean Society for Precision Engineering
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• v.10 no.2
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• pp.170-177
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• 1993

In robotic arc welding, the roll (rotation) of the torch about its direction vector does not have any effect on the welding operation. Thus we could use this redundant degree of greedom for the motion control of the robot manipulator. This paper presents an algorithm for the pseudo- inverse kinematic motion control of the 6-axis robot, which utilizes the above mentioned redunancy. The prototype welding operation and the tool path are also graphically simulated. Since the proposed algorithm requires only the position and normal vector of the weldine as an input data, it is useful for the CAD-based off-line programming of the arc welding robot. In addition, it also has the advantages of the redundant manipulator motion control, like singularity avoidance and collision free motion planning, when compared with the other motion control method based on the direct inverse kinematics.

• International conference on construction engineering and project management
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• 2015.10a
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• pp.158-160
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• 2015

Efficient scheduling and resource management are the key factor to reduce construction project budget (e.g., labor cost, equipment cost, material cost, etc.). Resource-based line of balance (LOB) technique has been used to complement the limitations of existing time-driven scheduling techniques (e.g., critical-path method). Optimizing construction alternatives contributes to cost savings while honoring the project deadline. However, existing LOB scheduling is lack of identifying optimal resource combination. This study presents a method which identifies the optimal construction alternatives, hence achieving resource minimization in a repetitive construction by using genetic algorithm (GA). The method provides efficient planning tool that enhances the usability of the system.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.9 no.3
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• pp.206-212
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• 2009

This paper presents an optimized integrated task planning and control approach for manipulating a nonholonomic robot by mobile manipulators. Then, we derive a kinematics model and a mobility of the mobile manipulator(M2) platform considering it as the combined system of the manipulator and the mobile robot. to improve task execution efficiency utilizing the redundancy, optimal trajectory of the mobile manipulator(M2) platform are maintained while it is moving to a new task point. A cost function for optimality can be defined as a combination of the square errors of the desired and actual configurations of the mobile robot and of the task robot. In the combination of the two square errors, a newly defined mobility of a mobile robot is utilized as a weighting index. With the aid of the gradient method, the cost function is minimized, so the path trajectory that the M2 platform generates is optimized. The simulation results of the 2 ink planar nonholonomic M2 platform are given to show the effectiveness of the proposed algorithm.

• KIPS Transactions on Computer and Communication Systems
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• v.5 no.7
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• pp.159-172
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• 2016

This paper presents a novel algorithm for searching evacuation paths in indoor disaster environments. The proposed method significantly improves the time complexity to find the paths to the evacuation exit by introducing a light-weight Disaster Evacuation Graph (DEG) for a building in terms of the size of the graph. With the DEG, the method also considers load balancing and bottleneck capacity of the paths to the evacuation exit simultaneously. The behavior of the algorithm consists of two phases: horizontal tiering (HT) and vertical tiering (VT). The HT phase finds a possible optimal path from anywhere of a specific floor to the evacuation stairs of the floor. Thus, after finishing the HT phases of all floors in parallel the VT phase begins to integrate all results from the previous HT phases to determine a evacuation path from anywhere of a floor to the safety zone of the building that could be the entrance or the roof of the building. It should be noted that the path produced by the algorithm. And, in order to define the range of graph to process, tiering scheme is used. In order to test the performance of the method, computing times and evacuation times are compared to the existing path searching algorithms. The result shows the proposed method is better than the existing algorithms in terms of the computing time and evacuation time. It is useful in a large-scale building to find the evacuation routes for evacuees quickly.

• Journal of the Korean Society of Industry Convergence
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• v.20 no.1
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• pp.49-55
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• 2017

This study proposes a new approach to Control and trajectory generation of a 8 DOF human robot arm with computational complexity and singularity problem. To deal with such problems, analytical methods for a redundant robot arm have been researched to enhance the performance of research, we propose an analytical kinematics algorithm for a 8 DOF bipped dual robot arm. Using this algorithm, it is possible to generate a trajectory passing through the singular points and intuitively move the elbow without regarding to the end-effector pose. Performance of the proposed algorithm was verified by simulation test with various conditions. It has been verified that the trajectory planning using this algorithm.

• Journal of the Semiconductor & Display Technology
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• v.14 no.2
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• pp.29-34
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• 2015

Traffic lights are common in cities and are important cues for the path planning of intelligent vehicles. In this paper, we propose a robust and efficient algorithm for recognizing traffic lights from video sequences captured by a low cost off-the-shelf camera. Instead of using color information for recognizing traffic lights, a shape based approach is adopted. In learning and detection phase, Histogram of Oriented Gradients (HOG) feature is used and a cascade classifier based on Adaboost algorithm is adopted as the main classifier for locating traffic lights. To decide the color of the traffic light, a technique based on histogram analysis in HSV color space is utilized. Experimental results on several video sequences from typical urban environment prove the effectiveness of the proposed algorithm.