• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.21 no.6
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• pp.1-20
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• 2022

The basic way to charge vehicles for using road and public transport networks is the entry-exit toll system. This system works by reading Hi-Pass and public transportation cards of the vehicles using card readers. However, the problems of navigating a route in consideration of entry-exit toll systems include the non-additive costs of enumerating routes. This problem is known as an NP-complete task that enumerates all paths and derives the optimal path. So far, the solution to the entry-exit toll system charging has been proposed in the form of transforming the road network. However, unlike in the public transport network where the cards are generalized, this solution has not been found in situations where network expansion is required with a transfer, multi-modes and multiple card readers. Hence, this study introduced the Link Label for a public transportation network composed of card readers in which network expansion is bypassed in selecting the optimal path by enumerating the paths through a one-to-one k-path search. Since the method proposed in this study constructs a relatively small set of paths, finding the optimal path is not burdensome in terms of computing power. In addition, the ease of comparison of sensitivity between paths indicates the possibility of using this method as a generalized means of deriving an optimal path.

• The Journal of Korea Robotics Society
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• v.17 no.4
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• pp.407-416
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• 2022

Multiple AMRs have been proved to be effective in improving warehouse productivity by eliminating workers' wasteful walking time. Although Multi-agent Path Finding (MAPF)-based solution is an optimal approach for this task, its deployment in practice is challenging mainly due to its imperfect plan-execution capabilities and insufficient computing resources for high-density environments. In this paper, we present a MAPF-based fleet management system architecture that robustly manages multiple robots by re-computing their paths whenever it is necessary. To achieve this, we defined four events that trigger our MAPF solver framework to generate new paths. These paths are then delivered to each AMR through ROS2 message topic. We also optimized a graph structure that effectively captures spatial information of the warehouse. By using this graph structure we can reduce computational burden while keeping its rescheduling functionality. With proposed MAPF-based fleet management system, we can control AMRs without collision or deadlock. We applied our fleet management system to the real logistics warehouse with 10 AMRs and observed that it works without a problem. We also present the usage statistic of adopting AMRs with proposed fleet management system to the warehouse. We show that it is useful over 25% of daily working time.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.10a
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• pp.354-360
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• 1998

In general, it is very difficult to find optimal fuzzy rules by experience when a system is dynamical and/or complex. Futhermore proper fuzzy partitioning is not deterministic and there is no unique solution. Therefore we propose a new design method of an optimal fuzzy logic controller, that is a co-evolutionary genetic algorithm finding optimal fuzzy rule and proper membership functions at the same time. We formalize the relation between fuzzy rules and membership functions in terms of fitness. We review the typical approaching methods to co-evolutionary genetic algorithms , and then classify them by fitness relation matrix. Applications of the proposed method to a path planning problem of autonomous mobile robots when moving objects exist are presented to demonstrate the performance and effectiveness of the method.

• Journal of Korean Society of Transportation
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• v.23 no.7 s.85
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• pp.149-158
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• 2005

The object of this paper is to develop the shortest path algorithm. The existing shortest path algorithm models are developed while considering travel time and travel distance. A few problems occur in these shortest path algorithm models, which have paid no regard to cognition of users, such as when user who doesn't have complete information about the trip meets a strange road or when the route searched from the shortest path algorithm model is not commonly used by users in real network. This paper develops a shortest path algorithm model to provide ideal route that many people actually prefer. In order to provide the ideal shortest path with the consideration of travel time, travel distance and road cognition, travel time is predicted by using Kalman filtering and travel distance is predicted by using GIS attributions. The road cognition is considered by using space data of GIS. Optimal routes provided from this paper are shortest distance path, shortest time path, shortest path considering distance and cognition and shortest path considering time and cognition.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.23 no.6
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• pp.33-39
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• 2023

Recently, reinforcement learning has been used to improve the communication performance of flying ad-hoc networks (FANETs) and to design mobility models. Mobility model is a key factor for predicting and controlling the movement of unmmaned aerial vehicle (UAVs). In this paper, we designed and analyzed the performance of Q-learning with fourier basis function approximation and Deep-Q Network (DQN) models for optimal path finding in a three-dimensional virtual environment where UAVs operate. The experimental results show that the DQN model is more suitable for optimal path finding than the Q-learning model in a three-dimensional virtual environment.

• Journal of the Korean Society of Industry Convergence
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• v.24 no.6_2
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• pp.891-897
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• 2021

In this paper, we propose a destination optimal route algorithm for providing route finding service for the transportation handicapped by using the multi-criteria decision-making technique and the modified A-STAR optimal route search algorithm. This is a method to set the route to the destination centering on safety by replacing the distance cost of the existing A-STAR optimal route search algorithm with the safety cost calculated through AHP/TOPSIS analysis. To this end, 10 factors such as road damage, curb, and road hole were first classified as poor road factors that hinder road driving, and then pairwise comparison of AHP was analyzed and then defined as the weight of TOPSIS. Afterwards, the degree of driving safety was quantified for a certain road section in Busan through TOPSIS analysis, and the development of an optimal route search algorithm for the transportation handicapped that replaces the distance cost with safety in the finally modified A-STAR optimal route algorithm was completed.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.169.5-169
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• 2001

This study proposes a neural network for solving optimization problems such as the TSP (Travelling Salesman Problem), scheduling, and line balancing. The Hopfield network has been used for solving such problems, but it frequently gives abnormal solutions or non-optimal ones. Moreover, the Hopfield network takes much time especially in solving large size problems. To overcome such disadvantages, this study adopts nodes whose outputs changes with a fixed value at every evolution. The proposed network is applied to solving a TSP, finding the shortest path for visiting all the cities, each of which is visted only once. Here, the travelling path is reflected to the energy function of the network. The proposed network evolves to globally minimize the energy function, and a ...

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.47 no.11
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• pp.1-6
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• 2010

In Sensor Networks, the problem of finding the optimal routing path dynamically, which passes through all terminals or nodes once per each, may come up. Providing a generalized scheme of approximations that can be applied to the kind of problems, and formulating the bounds of the run time and the results of the algorithm made from the scheme, one may evaluate mathematically the routing path formed in a given network. This paper, dealing with Euclidean TSP(Euclidean Travelling Sales Person) that represents such problems, provides the scheme for constructing the approximated Euclidean TSP by parallel computing, and the ground for determining the difference between the approximated Euclidean TSP produced from the scheme and the optimal Euclidean TSP.

• Journal of Korean Society of Transportation
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• v.33 no.2
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• pp.192-203
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• 2015

Pedestrians not only walk along pedestrian pathways, but also choose unusual routes such as passing through buildings or crossing large scale open spaces. This study defines these unusual paths as social path, and includes them into one of the pedestrian road categories. Previous pedestrian accessibility and route choice studies could not evaluate correctly the space connectivity or optimal route because the social path was not considered properly. Therefore, this study analyzes the effectiveness of the social path in view of space connectivity focused on Coex and Seoul stations in Seoul, which are representative transit oriented development(TOD) areas. Global integration, which is widely used in network analysis, is selected (as performance index) to identify the space hierarchy and define new pedestrian links. The study results show that the network connectivity is improved especially in the main streets and social paths. This study demonstrated that the social path should be considered in finding the pedestrian optimal route from the practical perspective.

• The Transactions of the Korea Information Processing Society
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• v.4 no.10
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• pp.2477-2484
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• 1997

Best-first heuristic search algorithm, such as $A^{\ast}$ algorithm, are one of the most important techniques used to solve many problems in artificial intelligence. A common feature of heuristic search is its high computational complexity, which prevents the search from being applied to problems is practical domains such as route-finding in road map with significantly many nodes. In this paper, several heuristic search algorithms are concerned. A new dynamic weighting heuristic method called the pat-sensitive heuristic is proposed. It is based on a dynamic weighting heuristic, which is used to improve search effort in practical domain such as admissible heuristic is not available or heuristic accuracy is poor. It's distinctive feature compared with other dynamic weighting heuristic algorithms is path-sensitive, which means that ${\omega}$(weight) is adjusted dynamically during search process in state-space search domain. For finding an optimal path, randomly scattered road-map is used as an application area.