• Journal of the Korea Society for Simulation
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• v.22 no.2
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• pp.53-62
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• 2013

Cloud environment is one in the field of distributed computing and it consists of physical nodes and virtual nodes. In distributed cloud environment, an optimal path search is that each node to perform a search for an optimal path. Synchronization of each node is required for the optimal path search via fast data transmission because of real-time environment. Therefore, a quantization technique is required in order to guarantee QoS(Quality of Service) and search an optimal path. The quantization technique speeds search data transmission of each node. So a main server can transfer data of real-time environment to each node quickly and the nodes can perform to search optimal paths smoothly. In this paper, we propose the quantization technique to solve the search problem. The quantization technique can reduce the total data transmission. In order to experiment the optimal path search system which applied the quantized data transmission, we construct a simulation of cloud environment. Quantization applied cloud environment reduces the amount of data that transferred, and then QoS of an application for the optimal path search problem is guaranteed.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.3D
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• pp.361-366
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• 2006

It is widely known that the dynamic optimal path algorithm, adopting real-time path finding, can be supporting an optimal route with which users are satisfied economically and accurately. However, this system has to search optimal routes frequently for updating them. The proposed concept of optimizing search area lets it reach heuristic optimal path rapidly and efficiently. Since optimal path should be increased in proportion to an distance between origin and destination, tremendous calculating time and highly efficient computers are required for searching long distance journey. In this paper, as a result of which the concepts of partial solution and representative path are suggested. It was possible to find an optimal route by decreasing a half area in comparison with the previous method. Furthermore, as the size of the searching area is uniform, comparatively low efficient computer is required for long distance trip.

• Journal of Institute of Control, Robotics and Systems
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• v.20 no.2
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• pp.138-142
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• 2014

This research presents an A* based algorithm which can be applied to Unmanned Ground Vehicle self-navigation in order to make the driving path smoother. Based on the grid map, A* algorithm generated the path by using straight lines. However, in this situation, the knee points, which are the connection points when vehicle changed orientation, are created. These points make Unmanned Ground Vehicle continuous navigation unsuitable. Therefore, in this paper, B-spline curve function is applied to transform the path transfer into curve type. And because the location of the control point has influenced the B-spline curve, the optimal control selection algorithm is proposed. Also, the optimal path tracking speed can be calculated through the curvature radius of the B-spline curve. Finally, based on this algorithm, a path created program is applied to the path results of the A* algorithm and this B-spline curve algorithm. After that, the final path results are compared through the simulation.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.28 no.8A
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• pp.581-590
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• 2003

This paper proposes the routing path selection mechanism for source routing-based PtMP (Point-to-Multipoint) call in ATM switching system. Especially, it suggests PtMP routing path selection method that can share the maximum resource prior to the optimal path selection, guarantee the reduction of path calculation time and cycle prevention. The searching for the nearest branch point from destination node to make the maximum share of resource is the purpose of this algorithm. Therefore among neighbor nodes from destination node by back-tracking, this algorithm fixes the node crossing first the node on existing path having the same Call ID as branch node, constructs the optimal PtMP routing path. The optimal node to be selected by back-tracking is selected by the use of Dijkstra algorithm. That is to say, PtMP routing path selection performs the step of cross node selection among neighboring nodes by back-tracking and the step of optimal node selection(optimal path calculation) among neighboring nodes by back-tracking. This technique reduces the process of search of routing information table for path selection and path calculation, also solves the cycle prevention easily during path establishment.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2007.11a
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• pp.176-179
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• 2007

In this paper, we develop the path planning algorithm using the improved Dijkstra algorithm and the particle swarm optimization. To get the optimal path, at first we construct the MAKLINK on the world environment and then make a graph associated with the MAKLINK. From the graph, we obtain the Dijkstra path between the starting point and the destination point. From the optimal path, we search the improved Dijkstra path using the graph. Finally, applying the particle swarm optimization to the improved Dijkstra path, we obtain the optimal path for the mobile robot. It turns out that the proposed method has better performance than the result in [1].

• Journal of Institute of Control, Robotics and Systems
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• v.16 no.9
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• pp.891-897
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• 2010

This paper proposes a cooperative control algorithm for a dual-arms robot which is carrying an object to the desired location. When the dual-arms robot is carrying an object from the start to the goal point, the optimal path in terms of safety, energy, and time needs to be selected among the numerous possible paths. In order to quantify the carrying efficiency of dual-arms, DAMM (Dual Arm Manipulability Measure) has been defined and applied for the decision of the optimal path. The DAMM is defined as the intersection of the manipulability ellipsoids of the dual-arms, while the manipulability measure indicates a relationship between the joint velocity and the Cartesian velocity for each arm. The cost function for achieving the optimal path is defined as the summation of the distance to the goal and inverse of this DAMM, which aims to generate the efficient motion to the goal. It is confirmed that the optimal path planning keeps higher manipulability through the short distance path by using computer simulation. To show the effectiveness of this cooperative control algorithm experimentally, a 5-DOF dual-arm robot with distributed controllers for synchronization control has been developed and used for the experiments.

• Journal of the Korean Society for Precision Engineering
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• v.16 no.12
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• pp.204-213
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• 1999

This paper suggests a numerical method of finding optimal geometric path and minimum-time motion for a manipulator arm. To find the minimum-time motion, the optimal geometric path is searched first, and the minimum-time motion is searched on this optimal path. In the algorithm finding optimal geometric path, the objective function is minimizing the combination of joint velocities, joint-jerks, and actuator forces as well as avoiding several static obstacles, where global search is performed by adjusting the seed points of the obstacle models. In the minimum-time algorithm, the traveling time is expressed by the linear combinations of finite-term quintic B-splines and the coefficients of the splines are obtained by nonlinear programming to minimize the total traveling time subject to the constraints of the velocity-dependent actuator forces. These two search algorithms are basically similar and their convergences are quite stable.

• 제어로봇시스템학회:학술대회논문집
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• 1992.10b
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• pp.79-84
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• 1992

Path planning is an important task for optimal motion of a robot in structured or unstructured environment. The goal of this paper is to plan the optimal collision-free path in 3D, when a robot is navigated to pick up some tools or to repair some parts from various locations. To accomplish the goal, the Path Coordinator is proposed to have the capabilities of an obstacle avoidance strategy and a traveling salesman problem strategy (TSP). The obstacle avoidance strategy is to plan the shortest collision-free path between each pair of n locations in 2D or in 3D. The TSP strategy is to compute a minimal system cost of a tour that is defined as a closed path navigating each location exactly once. The TSP strategy can be implemented by the Hopfield Network. The obstacle avoidance strategy in 2D can be implemented by the VGraph Algorithm. However, the VGraph Algorithm is not useful in 3D, because it can't compute the global optimality in 3D. Thus, the Path Coordinator is used to solve this problem, having the capabilities of selecting the optimal edges by the modified Genetic Algorithm and computing the optimal nodes along the optimal edges by the Recursive Compensation Algorithm.

• Journal of Ocean Engineering and Technology
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• v.20 no.1 s.68
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• pp.69-76
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• 2006

The design of efficient search path to maximize the Cumulative Detection Probability(CDP) is mainly dependent on experience and intuition when searcher detect the target using SONAR in the ocean. Recently with the advance of modeling and simulation method, it has been possible to access the optimization problems more systematically. In this paper, a method for the optimal search path calculation is developed based on the combination of the genetic algorithm and the calculation algorithm for detection range. We consider the discrete system for search path, space, and time, and use the movement direction of the SONAR for the gene of the genetic algorithm. The developed algorithm, OASPP(Optimal Acoustic Search Path Planning), is shown to be effective, via a simulation, finding the optimal search path for the case when the intuitive solution exists. Also, OASPP is compared with other algorithms for the measure of efficiency to maximize CDP.

• Journal of the Korean Institute of Intelligent Systems
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• v.18 no.2
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• pp.212-215
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• 2008

In this paper, we develop the optimal path planning algorithm using the improved Dijkstra algorithm and the particle swarm optimization. To get the optimal path, at first we construct the MAKLINK on the world environment and then make a graph associated with the MAKLINK. The MAKLINK is a set of edges which consist of the convex set. Some of the edges come from the edges of the obstacles. From the graph, we obtain the Dijkstra path between the starting point and the destination point. From the optimal path, we search the improved Dijkstra path using the graph. Finally, applying the particle swarm optimization to the improved Dijkstra path, we obtain the optimal path for the mobile robot. It turns out that the proposed method has better performance than the result in [1] through the experiment.