• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.8
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• pp.2473-2489
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• 2022

Urban development has brought about the increasing saturation of urban traffic demand, and traffic congestion has become the primary problem in transportation. Roads are in a state of waiting in line or even congestion, which seriously affects people's enthusiasm and efficiency of travel. This paper mainly studies the discrete domain path planning method based on the flow data. Taking the traffic flow data based on the highway network structure as the research object, this paper uses the deep learning theory technology to complete the path weight determination process, optimizes the path planning algorithm, realizes the vehicle path planning application for the expressway, and carries on the deployment operation in the highway company. The path topology is constructed to transform the actual road information into abstract space that the machine can understand. An appropriate data structure is used for storage, and a path topology based on the modeling background of expressway is constructed to realize the mutual mapping between the two. Experiments show that the proposed method can further reduce the interpolation error, and the interpolation error in the case of random missing is smaller than that in the other two missing modes. In order to improve the real-time performance of vehicle path planning, the association features are selected, the path weights are calculated comprehensively, and the traditional path planning algorithm structure is optimized. It is of great significance for the sustainable development of cities.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2007.04a
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• pp.78-83
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• 2007

The paper focuses on the establishment of optimized bucket path planning and trajectory control designated for force-reflecting backhoe reacting to excavation environment, such as potential obstacles and ground characteristics. The developed path planning method can be used for precise bucket control, and more importantly for obstacle avoidance which is directly related to safety issues. The platform of this research was based on conventional papers regarding the kinematic model of excavator. Jacobian matrix was constructed to find optimal joint angles and rotation angles of bucket from position and orientation data of excavator. By applying Newton-Raphson method optimal joint angles and bucket orientation were derived simultaneously in the way of minimizing positional errors of excavator. The model presented in this paper was intended to function as a cornerstone to build complete and advanced path planning of excavator by implementing soil mechanics and further study of excavator dynamics together.

• Korean Journal of Computational Design and Engineering
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• v.17 no.4
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• pp.246-252
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• 2012

The piping route of hydraulic hose is designed with avoiding interferences to surrounding components. However, in a real practice, the piping route is mostly decided with an expert's experiences on site due to the complexity of design. Thus, this paper proposes a design methodology of the optimized route of a hose. We use NURBS representation to describe the piping route, which is possible to be locally modified, and an energy minimization method is applied to avoid interferences to the surroundings. In other words, the NURBS curve describing a piping route is modified to meet the desired positions from minimizing the perturbation of the control points, and the strain energy of the curve is then optimized to make the curve natural. The proposed method is implemented and its feasibility is validated using the commercial CAD software, CATIA V5.

• Journal of Institute of Control, Robotics and Systems
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• v.20 no.9
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• pp.936-941
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• 2014

Walking mechanisms are very important for legged robots to ensure their stable locomotion. In this research, Klann-linkage is suggested as a walking mechanism for a water-running robot and is optimized using level average analysis. The structure of the Klann-linkage is introduced first and design variables for the Klann-linkage are identified considering the kinematic task of the walking mechanism. Next, the design problem is formulated as a path generation optimization problem. Specifically, the desired path for the foot-pad is defined and the objective function is defined as the structural error between the desired and the generated paths. A process for solving the optimization problem is suggested utilizing the sensitivity analysis of the design variables. As a result, optimized lengths of Klann-linkage are obtained and the optimum trajectory is obtained. It is found that the optimized trajectory improves the cost function by about 62% from the initial one. It is expected that the results from this research can be used as a good example for designing legged robots.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.869-874
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• 2004

The inverse kinematics of five-axis milling machines produce large errors near stationary points of the required surface. When the tool travels cross or around the point the rotation angles may jump considerably leading to unexpected deviations from the prescribed trajectories. We propose three new algorithms to repair the trajectories by adjusting the rotation angles in such a way that the kinematics error is minimized. Given the tool orientations and the inverse kinematics of the machine, we first eliminate the jumping angles exceeding ${\pi}$ by using the angle adjustment algorithm, leaving the jumps less than ${\pi}$ to be further optimized. Next, we propose to apply an angle switching algorithm to compute the rotations and identify an optimized sequence of rotations by the shortest path scheme. Further error reduction is accomplished by the angle insertion algorithm based an o special interpolation to obtain the required rotations near the singularity. We have verified the algorithms by five-axis milling machines, namely, MAHO600E at the CIM Lab of Asian Institute of Technology and HERMLE UWF902H at the CIM Lab of Kasetsart University.

• Journal of the Korean Society of Hazard Mitigation
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• v.9 no.4
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• pp.45-51
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• 2009

The paper presents the economic analysis of optimized-rehabilitation area method considered as one of the preventive maintenance methods in asphalt concrete pavement. The optimized-rehabilitation area was selected based on the analysis of traffic lane characteristics. The main concept of the selected method was to minimize the maintenance cost. The effective width of traffic lane in this method was 70 cm of each wheel path. According to the traffic survey conducted in this research, more than 95% of vehicles passed within the width of each wheel path. The new preventive optimized-rehabilitation area method showed less maintenance cost than the conventional overlay. In addition, traffic congestions and the user cost can be reduced. The research results revealed that the total maintenance cost was reduced by 35% by using the new method compare to the conventional one.

• Journal of Computational Design and Engineering
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• v.3 no.2
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• pp.132-139
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• 2016

In this paper, a minimum time path planning strategy is proposed for multi points manufacturing problems in drilling/spot welding tasks. By optimizing the travelling schedule of the set points and the detailed transfer path between points, the minimum time manufacturing task is realized under fully utilizing the dynamic performance of robotic manipulator. According to the start-stop movement in drilling/spot welding task, the path planning problem can be converted into a traveling salesman problem (TSP) and a series of point to point minimum time transfer path planning problems. Cubic Hermite interpolation polynomial is used to parameterize the transfer path and then the path parameters are optimized to obtain minimum point to point transfer time. A new TSP with minimum time index is constructed by using point-point transfer time as the TSP parameter. The classical genetic algorithm (GA) is applied to obtain the optimal travelling schedule. Several minimum time drilling tasks of a 3-DOF robotic manipulator are used as examples to demonstrate the effectiveness of the proposed approach.

• Journal of Institute of Control, Robotics and Systems
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• v.17 no.5
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• pp.405-411
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• 2011

This paper presents a hierarchical trajectory planning method which can handle a collision-free of the planned path in complex and dynamic environments. A PV (Path & Velocity profile) planning method minimizes a sharp change of orientation and waiting time to avoid a collision with moving obstacle through detour path. The path generation problem is solved by three steps. In the first step, a smooth global path is generated using $A^*$ algorithm. The second step sets up the velocity profile for the optimization problem considering the maximum velocity and acceleration. In the third step, the velocity profile for obtaining the shortest path is optimized using the fuzzy and genetic algorithm. To show the validity and effectiveness of the proposed method, realistic simulations are performed.

• Journal of the Korea Society of Computer and Information
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• v.22 no.6
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• pp.49-55
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• 2017

Robots, games and life applications have been developed while computer areas are developed. Moreover, various applications have been utilized for various users including the early childhood. Recently, smart phones have been dramatically used by various users including early childhood. Many applications need to find a path from a starting point to destinations. For example, without using real maps, users can find the direct paths for the destinations in realtime. Specifically, path exploration in game programs is so important to have accurate results. Nowadays, with these techniques, diverse applications for educations of early childhood have been developed. To deal with the functions, necessity of efficient path search programs with high accuracy becomes much higher. In this paper, we design and develop a friendly maze program for early childhood based on a path searching algorithm. Basically, the path of lineal distance from a starting location to destination is considered. Moreover, weight values are calculated by considering heuristic weighted h(x). In our approach, A* algorithm searches the path considering weight values. Moreover, we utilize depth first search approach instead of breadth first search in order to reduce the search space. so it is proper to use A* algorithm in finding efficient paths although it is not optimized paths.

• International conference on construction engineering and project management
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• 2022.06a
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• pp.1085-1092
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• 2022

Travel distance is a parameter mainly used in the objective function of Construction Site Layout Planning (CSLP) automation models. To obtain travel distance, common approaches, such as linear distance, shortest-distance algorithm, visibility graph, and access road path, concentrate only on identifying the shortest path. However, humans do not necessarily follow one shortest path but can choose a safer and more comfortable path according to their situation within a reasonable range. Thus, paths generated by these approaches may be different from the actual paths of the workers, which may cause a decrease in the reliability of the optimized construction site layout. To solve this problem, this paper adopts reinforcement learning (RL) inspired by various concepts of cognitive science and behavioral psychology to generate a realistic path that mimics the decision-making and behavioral processes of wayfinding of workers on the construction site. To do so, in this paper, the collection of human wayfinding tendencies and the characteristics of the walking environment of construction sites are investigated and the importance of taking these into account in simulating the actual path of workers is emphasized. Furthermore, a simulation developed by mapping the identified tendencies to the reward design shows that the RL agent behaves like a real construction worker. Based on the research findings, some opportunities and challenges were proposed. This study contributes to simulating the potential path of workers based on deep RL, which can be utilized to calculate the travel distance of CSLP automation models, contributing to providing more reliable solutions.