• Proceedings of the Korea Society for Industrial Systems Conference
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• 2006.05a
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• pp.95-108
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• 2006

Because of rapid expansion of third party logistics, fierce competition in the transportation industry, and the diversification and globalization of transportation channels, an effective transportation planning by means of multimodal transport is badly needed. Accordingly, this study aims to suggest an optimal transport algorithm for the multimodal transport in the international logistics. Cargoes and stopovers can be changed numerously according to the change of transportation modes, thus being a NP-hard problem. As a solution for this problem, first of all, we have applied a pruning algorithm to simplify it, suggesting a heuristic algorithm for constrained shortest path problem to find out a feasible area with an effective time range and effective cost range, which has been applied to the Label Setting Algorithm, consequently leading to multiple Pareto optimal solutions. Also, in order to test the efficiency of the algorithm for constrained shortest path problem, this paper has applied it to the actual transportation path from Busan port of Korea to Rotterdam port of Netherlands.

• Transactions of the Korean Society of Mechanical Engineers
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• v.15 no.1
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• pp.69-77
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• 1991

A collision-free trajectory planning algorithm using an iterative learning concept is proposed for dual robot arms in a 3-D common workspace to accurately follow their specified paths with constant velocities. Specifically, a collision-free trajectory minimizing the trajectory error is obtained first by employing the linear programming technique. Then the total operating time is iteratively adjusted based on the maximum trajectory error of the previous iteration so that the collision-free trajectory has no deviation from the specified path and also that the operating time is near-minimal. To show the validity of the proposed algorithm, a numerical example is presented based on two planar robots.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.28B no.5
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• pp.357-368
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• 1991

Collision-free trajectory planning for two robots is considered. The two robot system handled in the paper is given specified geometric paths for two robots, and the task is repeating. Then, the robot dynamics is transformed as a function of the traveled lengths along the paths, and the bounds on acceleration and velocity are described in the phase plane be taking the constraints on torques and joint velocities into consideration. Collision avoidance and time optimality are considered simultaneously in the coordination space and the phase plane, respectively. The proof for the optimality of the proposed algorithm is given, and a simulation result is included to show the usefulness of the proposed method.

• Journal of the Korean Society for Precision Engineering
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• v.15 no.9
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• pp.101-110
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• 1998

In process planning for 3D pocket machining, the critical issues for the optimal process planning are the generation of cutting layers and the tool selection for each cutting layers as well as the other factors such as the determination of machining types, tool path, etc. This paper describes the optimal tool selection on a single cutting layer for 2D pocket machining, the generation of cutting layers for 3D pocket machining, the determination of the thickness of each cutting layers, the determination of the tool combinations for each cutting layers and also the development of an algorithm for determining the machining sequence which reduces the number of tool exchanges, which are based on the backward approach. The branch and bound method is applied to select the optimal tools for each cutting layer, and an algorithmic procedure is developed to determine the machining sequence consisting of the pairs of the cutting layers and cutting tools to be used in the same operation.

• 제어로봇시스템학회:학술대회논문집
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• 1989.10a
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• pp.627-634
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• 1989

A collision-free trajectory planning algorithm using the iterative learning concept is proposed for dual robot arms in a 3-D workspace to accurately follow their specified paths with constant velocities. Specifically, a collision-free trajectory minimizing the trajectory error is obtained first by employing the linear programming technique. Then the total operating time is iteratively adjusted based on the maximum trajectory error of the previous iteration so that the collision-free trajectory has no deviation from the specified path and also the operating time is near-minimal.

• Journal of IKEEE
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• v.27 no.1
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• pp.1-11
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• 2023

Path driving tests are necessary for the development of self-driving cars or robots. These tests are being conducted in simulation as well as real environments. In particular, for development using reinforcement learning and deep learning, development through simulators is also being carried out when data of various environments are needed. To this end, it is necessary to utilize not only manually designed paths but also various randomly and automatically designed paths. This test site design can be used for actual construction and manufacturing. In this paper, we introduce a method for randomly generating a driving test path consisting of a combination of arcs and segments. This consists of a method of determining whether there is a collision by obtaining the distance between an arc and a line segment, and an algorithm that deletes part of the path and recreates an appropriate path if it is impossible to continue the path.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.43 no.6
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• pp.488-496
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• 2015

This paper proposes a framework to determine the routes of multiple unmanned aerial vehicles (UAVs) to conduct multiple tasks in different locations considering the survivability of the vehicles. The routing problem can be formulated as the vehicle routing problem (VRP) with different cost matrices representing the trade-off between the safety of the UAVs and the mission completion time. The threat level for a UAV at a certain location was modeled considering the detection probability and the shoot-down probability. The minimal-cost path connecting two locations considering the threat level and the flight distance was obtained using the Dijkstra algorithm in hexagonal cells. A case study for determining the optimal routes for a persistent multi-UAVs surveillance and reconnaissance missions given multiple enemy bases was conducted and its results were discussed.

• Journal of rehabilitation welfare engineering & assistive technology
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• v.11 no.4
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• pp.315-321
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• 2017

This paper is a study on the design and realization of Pedestrian navigation service system for the visually impaired. As it is an user interface considering visually impaired, voice recognition functioned smartphone was used as the input tool and the Osteoacusis headset, which can vocally guide directions while recognizing the surrounding environment sound, was used as the output tool. Unlike the pre-existing pedestrian navigation smartphone apps, the developed system guides walking direction by the scale of the left and right stereo sound of the headset wearing, and the voice guidance about the forked or curved path is given several meters before according to the speed of the user, and the user is immediately warned of walking opposite direction or proceeding off the path. The system can acquire stable and reliable directional information using the motion tracker with the dynamic heading accuracy of 1.5 degrees. In order to overcome GPS position error, we proposed a robust trajectory planning algorithm for position error. Experimental results for the developed system show that the average directional angle error is 6.82 degrees (standard deviation: 5.98) in the experimental path, which can be stated that it stably navigated the user relatively.

• Nuclear Engineering and Technology
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• v.56 no.4
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• pp.1153-1164
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• 2024

In recent years, unmanned ground vehicles (UGVs) have been used to search for lost or stolen radioactive sources to avoid radiation exposure for operators. To achieve autonomous localization of radioactive sources, the UGVs must have the ability to automatically determine the next radiation measurement location instead of following a predefined path. Also, the radiation field of radioactive sources has to be reconstructed or inverted utilizing discrete measurements to obtain the radiation intensity distribution in the area of interest. In this study, we propose an effective source localization framework and method, in which UGVs are able to autonomously explore in the radiation area to determine the location of radioactive sources through an iterative process: path planning, radiation field reconstruction and estimation of source location. In the search process, the next radiation measurement point of the UGVs is fully predicted by the design path planning algorithm. After obtaining the measurement points and their radiation measurements, the radiation field of radioactive sources is reconstructed by the Gaussian process regression (GPR) model based on machine learning method. Based on the reconstructed radiation field, the locations of radioactive sources can be determined by the peak analysis method. The proposed method is verified through extensive simulation experiments, and the real source localization experiment on a Cs-137 point source shows that the proposed method can accurately locate the radioactive source with an error of approximately 0.30 m. The experimental results reveal the important practicality of our proposed method for source autonomous localization tasks.

• Journal of KIISE:Software and Applications
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• v.37 no.8
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• pp.641-646
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• 2010

This paper presents algorithms for real-time navigation of a humanoid robot with a stereo vision but no other sensors. Using the algorithms, a robot can recognize its 3D environment by retrieving SIFT features from images, estimate its position through the Kalman filter, and plan its path to reach a destination avoiding obstacles. Our approach focuses on estimating the robot’s central walking path trajectory rather than its actual walking motion by using an approximate model. This strategy makes it possible to apply mobile robot localization approaches to humanoid robot localization. Simple collision free path planning and motion control enable the autonomous robot navigation. Experimental results demonstrate the feasibility of our approach.