• Journal of Aerospace System Engineering
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• v.14 no.2
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• pp.1-11
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• 2020

Detecting obstacles and generating a suitable path to avoid obstacles in real time is a prime mission requirement for UAVs. In areas, close to buildings and people, detecting obstacles in the path and estimating its own position (egomotion) in GPS degraded/denied environments are usually addressed with vision-based Simultaneous Localization and Mapping (SLAM) techniques. This presents possibilities and challenges for the feasible path generation with constraints of vehicle dynamics in the configuration space. In this paper, a near real-time feasible path is shown to be generated in the ORB-SLAM framework using a chain-based path planning approach in a force field with dynamic constraints on path length and minimum turn radius. The chain-based path plan approach generates a set of nodes which moves in a force field that permits modifications of path rapidly in real time as the reward function changes. This is different from the usual approach of generating potentials in the entire search space around UAV, instead a set of connected waypoints in a simulated chain. The popular ORB-SLAM, suited for real time approach is used for building the map of the environment and UAV position and the UAV path is then generated continuously in the shortest time to navigate to the goal position. The principal contribution are (a) Chain-based path planning approach with built in obstacle avoidance in conjunction with ORB-SLAM for the first time, (b) Generation of path with minimum overheads and (c) Implementation in near real time.

• Proceedings of the Korean Society for Technology of Plasticity Conference
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• 2002.05a
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• pp.44-47
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• 2002

In order to make a doubly curved sheet metal effectively, a sheet metal forming process has been developed by adopting the flexibility of the incremental forming process and the principle of bending deformation which causes slight deformation to thickness. The developed process is an unconstrained forcing process with no holder. For this study, the experimental equipment is set up with the roll set which consists of two pairs of support rolls and one center roll. In the experiments using aluminum sheets and FEM simulation, it is found that the curvature of the formed sheet metal is determined by controlling the distance between supporting rolls in pairs and the forming depth of the center roll. The FEM simulation of the forming process using the roll set along the one path shows the distributions of the curvatures in two directions along the path, and gives information about the characteristics of the proposed forming process.

• Journal of applied mathematics & informatics
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• v.39 no.5_6
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• pp.689-701
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• 2021

The purpose of this paper is to optimize the number of source places required for the unique representation of the destination using the tools of graph theory. A subset S of vertices of a graph G is called a rational resolving set of G if for each pair u, v ∈ V - S, there is a vertex s ∈ S such that d(u/s) ≠ d(v/s), where d(x/s) denotes the mean of the distances from the vertex s to all those y ∈ N[x]. A rational resolving set is called minimal rational resolving set if no proper subset of it is a rational resolving set. In this paper we study varieties of minimal rational resolving sets defined on the basis of its complements and compute the minimum and maximum cardinality of such sets, respectively called as lower and upper rational metric dimensions for power of a path P_n analysing various possibilities.

• Journal of Institute of Control, Robotics and Systems
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• v.11 no.9
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• pp.802-809
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• 2005

A hybrid genetic algorithm is proposed for the problem of generating laser torch paths to cut a stock plate nested with free-formed parts each having a set of sub-parts. In the problem, the total unproductive travel distance of the torch is minimized. The problem is shown to be formulated as a special case of the standard travelling salesman problem. The hybrid genetic algorithm for solving the problem is hierarchically structured: First, it uses a genetic algorithm to find the cutting path f3r the parts and then, based on the obtained cutting path, sequence of sub-parts and their piercing locations are optimally determined by using a combined genetic and heuristic algorithms. This process is repeated until any progress in the total unproductive travel distance is not achieved. Computational results are provided to illustrate the validity of the proposed algorithm.

• International Journal of Aeronautical and Space Sciences
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• v.10 no.2
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• pp.1-11
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• 2009

In this paper, two algorithms to solve the path planning problem with constraints from obstacles are presented. One proposed Algorithm is "Grid point-based path planning". The first step of this algorithm is to set points which can be the waypoints around the field. These points can be located inside or outside of the field or the obstacles. Therefore, we should determine whether those points are located in the field or not. Using the equations of boundary lines for a region that we are interested in is an effective approach to handle. The other algorithm is based on the boundary lines of the agricultural field, and the concept of this algorithm is well known as "boustrophedon method". These proposed algorithms are simple but powerful for complex cases since it can generate a plausible path for the complex shape which cannot be represented by using geometrical approaches efficiently and for the case that some obstacles or forbidden regions are located on the field by using a skill of discriminants about set points. As will be presented, this proposed algorithm could exhibit a reasonable accuracy to perform an agricultural mission.

• International conference on construction engineering and project management
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• 2022.06a
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• pp.319-327
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• 2022

Robots for construction sites, although not deeply widespread, are finding applications in the duties of project monitoring, material movement, documentation, security, and simple repetitive construction-related tasks. A significant shortcoming in the use of robots is the complexity involved in programming and re-programming an automation routine. Robotic programming is not an expected skill set of the traditional construction industry professional. Therefore, this research seeks to deliver a low-cost approach toward re-programming that does not involve a programmer's skill set. The researchers in this study examined an approach toward programming a terrestrial-based drone so that it follows a taped path. By doing so, if an alternative path is required, programmers would not be needed to re-program any part of the automated routine. Changing the path of the drone simply requires removing the tape and placing a different path - ideally simplifying the process and quickly allowing practitioners to implement a new automated routine. Python programming scripts were used with a DJI Robomaster EP Core drone, and a terrain navigation assessment was conducted. The study examined the pass/fail rates for a series of trial run over different terrains. The analysis of this data along with video recording for each trial run allowed the researchers to conclude that the accuracy of the tape follow technique was predictable on each of the terrain surfaces. The accuracy and predictability inform a non-coding construction practitioner of the optimal placement of the taped path. This paper further presents limitations and suggestions for some possible extended research options for this study.

• Journal of the Korean Data and Information Science Society
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• v.16 no.1
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• pp.1-11
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• 2005

A network consisting of a set of N nodes and a set of links is considered. The nodes are assumed to be perfect and the states of links to be binary and associated to each other. After defining a network structure function, which represents the state of network as a function of the states of links, we obtain some lower and upper bounds on the network reliability by adopting minmax principle and minimal path and cut set arguments. These bounds are given as functions of the reliabilities of links. The bridge network is considered as an example.

• Proceedings of the KIEE Conference
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• 1996.07b
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• pp.871-874
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• 1996

It is practically very important in multi-criteria long-tenn IRP process to develop the set of candidate resource plans that meet following two conditions. First, the number of resource plans should not be too large for computer job. Second, the unknown best plan should exist among the set of candidate plans. In this paper, several novel procedures are developed. The main idea of the procedures is the two-stage approach. The first stage is to find the set of feasible resource capacity mixes in given target year(s). And, the second stage is to find the set of alternative paths of resource options for each resource mix determined in the first stage.

• International journal of advanced smart convergence
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• v.10 no.1
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• pp.56-61
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• 2021

Neighbor path selection is to pre-select alternate routes in case geographically correlated failures occur simultaneously on the communication network. Conventional heuristic-based algorithms no longer improve solutions because they cannot sufficiently utilize historical failure information. We present a novel solution model for neighbor path selection by using machine learning technique. Our proposed machine learning neighbor path selection (ML-NPS) model is composed of five modules- random graph generation, data set creation, machine learning modeling, neighbor path prediction, and path information acquisition. It is implemented by Python with Keras on Tensorflow and executed on the tiny computer, Raspberry PI 4B. Performance evaluations via numerical simulation show that the neighbor path communication success probability of our model is better than that of the conventional heuristic by 26% on the average.

• The Journal of Korea Robotics Society
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• v.18 no.1
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• pp.1-9
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• 2023

This paper presents an optimal path planning strategy for aerial searching and surveying of a user-designated area using multiple Unmanned Aerial Vehicles (UAVs). The method is designed to deal with a single unseparated polygonal area, regardless of polygonal convexity. By defining the search area into a set of grids, the algorithm enables UAVs to completely search without leaving unsearched space. The presented strategy consists of two main algorithmic steps: cellular decomposition and path planning stages. The cellular decomposition method divides the area to designate a conflict-free subsearch-space to an individual UAV, while accounting the assigned flight velocity, take-off and landing positions. Then, the path planning strategy forms paths based on every point located in end of each grid row. The first waypoint is chosen as the closest point from the vehicle-starting position, and it recursively updates the nearest endpoint set to generate the shortest path. The path planning policy produces four path candidates by alternating the starting point (left or right edge), and the travel direction (vertical or horizontal). The optimal-selection policy is enforced to maximize the search efficiency, which is time dependent; the policy imposes the total path-length and turning number criteria per candidate. The results demonstrate that the proposed cellular decomposition method improves the search-time efficiency. In addition, the candidate selection enhances the algorithmic efficacy toward further mission time-duration reduction. The method shows robustness against both convex and non-convex shaped search area.