• Transactions of the Korean Society of Mechanical Engineers A
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• v.20 no.10
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• pp.3160-3172
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• 1996

A search algorithm for the collision free, time optimal transport path of overhead cranes has been proposed in this paper. The map for the working environment of overhead cranes was constructed in the form of three dimensional grid. The obstacle occupied region and unoccupied region of the map has been represented using the octree model. The best-first search method with a suitable estimation function was applied to select the knot points on the collision free transport path to the octree model. The optimization technique, minimizing the travel time required for transporting objects to the goal while subjected to the dynamic constraints of the crane system, was developed to find the smooth time optimal path in the form of cubic spline functions which interpolate the selected knot points. Several simulation results showed that the selected estimation function worked effectively insearching the knot points on the collision free transport path and that the resulting transport path was time optimal path while satisfying the dynamic constraints of the crane system.

• Proceedings of the KIEE Conference
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• 2006.10c
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• pp.551-553
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• 2006

An autonomous navigation technology for the mobile robot is investigated in this paper. The proposed robot path planning algorithm employs the dynamic programming to find the optimal path. The algorithm finds the global optimal path through the local computation on the environmental map. Since the robot computes the new path at every point, it can avoid the obstacle successfully during the navigation. The experimental results of the robot navigation are included in this paper.

• Journal of the Korean Operations Research and Management Science Society
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• v.16 no.1
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• pp.1-12
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• 1991

This paper studies an optimal algorithm for the Maximum Origin-Destination Flor Path (MODFP) in an acyclic transportation network. We define a Pseudo-Flow each are so that it can give an upper bound to the total flow of a given path. And using the K-th Shortest Path algorithm we obtain upper bound of MODF which is decreasing as the number of searched path grows. Computational Complexity of optimal algorithm is O(K + m) $n_{2}$), K being the total number of searched path. We proved that the problem complexity of finding MODFP in an acyclic network is NP-hard, showing that the-satisfiability problem can be polynomialy reduced to this problem. And we estimated the average of the number K as being (m/n)$^{1,08}$ Exp (0.00689gm) from the computational experiments.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.1419-1423
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• 2004

In this paper, we propose an algorithm for planning an optimal path to capture a moving object by a mobile robot in real-time. The direction and rotational angular velocity of the moving object are estimated using the Kalman filter, a state estimator. It is demonstrated that the moving object is tracked by using a 2-DOF active camera mounted on the mobile robot and then captured by a mobile manipulator. The optimal path to capture the moving object is dependent on the initial conditions of the mobile robot, and the real-time planning of the robot trajectory is definitely required for the successful capturing of the moving object. Therefore the algorithm that determines the optimal path to capture a moving object depending on the initial conditions of the mobile robot and the conditions of a moving object is proposed in this paper. For real-time implementation, the optimal representative blocks have been utilized for the experiments to show the effectiveness of the proposed algorithm.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.186-190
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• 2003

In this paper, we propose an algorithm for planning an optimal path to capture a moving object by a mobile robot in real-time. The direction and rotational angular velocity of the moving object are estimated using the Kalman filter, a state estimator. It is demonstrated that the moving object is tracked by using a 2-DOF active camera mounted on the mobile robot and then captured by a mobile manipulator. The optimal path to capture the moving object is dependent on the initial conditions of the mobile robot, and the real-time planning of the robot trajectory is definitely required for the successful capturing of the moving object. Therefore the algorithm that determines the optimal path to capture a moving object depending on the initial conditions of the mobile robot and the conditions of a moving object is proposed in this paper. For real-time implementation, the optimal representative blocks have been utilized for the experiments to show the effectiveness of the proposed algorithm.

• Journal of Korean Society of Transportation
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• v.14 no.1
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• pp.51-67
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• 1996

The optimal path-finding problem becomes complicated when multiple variables are simultaneously considered such as physical route length, degree of congestion, traffic capacity of intersections, number of intersections and lanes, and existence of free ways. Therefore, many researchers in various fields (management science, computer science, applied mathematics, production planning, satellite launching) attempted to solve the problem by ignoring many variables for problem simplification, by developing intelligent algorithms, or by developing high-speed hardware. In this research, an integration of expert system technique and case-based reasoning in high level with a conventional algorithms in lower level was attempted to develop an optimal path-finding system. Early application of experienced driver's knowledge and case data accumulated in case base drastically reduces number of possible combinations of optimal paths by generating promising alternatives and by eliminating non-profitable alternatives. Then, employment of a conventional optimization algorithm provides faster search mechanisms than other methods such as bidirectional algorithm and $A^*$ algorithm. The conclusion obtained from repeated laboratory experiments with real traffic data in Seoul metropolitan area shows that the integrated approach to finding optimal paths with consideration of various real world constraints provides reasonable solution in a faster way than others.

• Proceedings of the KIEE Conference
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• 2005.10b
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• pp.174-176
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• 2005

This treatise studies composing process that renew information mastered by interactive experience between environment and system via network among individuals. In the previous study map information regarding free space is learned by using of reinforced learning algorithm, which enable each individual to construct optimal action policy. Based on those action policy each individuals can obtain optimal path. Moreover decision process to distinguish best optimal path by comparing those in the network composed of each individuals is added. Also information about the finally chosen path is being updated. A self renewing method of each system information by sharing the each individual data via network is proposed Data enrichment by shilling the information of many maps not in the single map is tried Numerical simulation is conducted to confirm the propose concept. In order to prove its suitability experiment using micro-mouse by integrating and comparing the information between individuals is carried out in various types of map to reveal successful result.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.253-253
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• 2000

Each robot plays a role of its own behavior in dynamic robot-soccer environment. One of the most necessary conditions to win a game is control of robot movement. In this paper we suggest a win strategy using Cellular Neural Network to set optimal path and cooperative behavior, which divides a soccer ground into grid-cell based ground and has robots move a next grid-cell along the optimal path to approach the moving target.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1993.06a
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• pp.1258-1261
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• 1993

This paper presents a methodology of path planning and navigation for an autonomous mobile robot. A fast algorithm using decomposition technique, which computes the optimal paths between all pairs of nodes, is proposed for real-time calculation. The robot is controlled by fuzzy approximation reasoning. Our new methodology has been implemented on a mobile robot. The results show that the robot successfully navigates to its destination following the optimal path.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.9 no.2
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• pp.454-461
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• 2005

Car navigation system which is killer application fuses map management techniques into CPS techniques. Even if the existing navigation systems are designed for the shortest path, they are not able to cope efficiently with the change of the traffic flow and the bottleneck point of road. Therefore, it is necessary to find out shortest path algorithm based on time instead of distance which takes traffic information into consideration. In this paper, we propose a optimal path search algorithm based on the traffic information. More precisely. we introduce the system architecture for finding out optimal paths, and the limitations of the existing shortest path search algorithm are also analyzed. And then, we propose a new algorithm for finding out optimal path to make good use of the orientation of the collected traffic information.