• Journal of the Korean Society for Precision Engineering
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• v.13 no.6
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• pp.122-127
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• 1996

The achievement of the optimal condition for the task of an industrial articulated robot used in many fields is an important problem to improve productivity. In this paper, a minimum-time trajectory for an articulated robot along the specified path is studied and simulated with a proper example. A general dynamic model of manipulator is represented as a function of path distance. Using this model, the velocity is produced as fast as possible at each point along the path. This minimum-time trajectory planning module together with the existing collision-free path planning modules is utilized to design the optimal path planning of robot in cases where obstacles present.

• 제어로봇시스템학회:학술대회논문집
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• 1990.10b
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• pp.990-995
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• 1990

A minimum-time trajectory planning for two robot arms with designated paths and coordination is proposed. The problem considered in this paper is a subproblem of hierarchically decomposed trajectory planning approach for multiple robots : i) path planning, ii) coordination planning, iii) velocity planning. In coordination planning stage, coordination space, a specific form of configuration space, is constructed to determine collision region and collision-free region, and a collision-free coordination curve (CFCC) passing collision-free region is selected. In velocity planning stage, normal dynamic equations of the robots, described by joint angles, velocities and accelerations, are converted into simpler forms which are described by traveling distance along collision-free coordination curve. By utilizing maximum allowable torques and joint velocity limits, admissible range of velocity and acceleration along CFCC is derived, and a minimum-time velocity planning is calculated in phase plane. Also the planning algorithm itself is converted to simple numerical iterative calculation form based on the concept of neural optimization network, which gives a feasible approximate solution to this planning problem. To show the usefulness of proposed method, an example of trajectory planning for 2 SCARA type robots in common workspace is illustrated.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.20 no.1
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• pp.177-186
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• 2020

This paper deals with the car paint sequencing problem (CPSP) that the entrance sequence is to same colored group with maximum sequenced cars for the buffer arriving cars from the body shop. This problem classified by NP-complete problem because of the exact solution has not obtained within polynomial time. CPSP is aim to minimum pugging number that each pugging must be performs at color changing time in order to entirely cleaning the remaining previous color. To be obtain the minimum number of moving distance with window concept and minimum number of pugging, this paper sorts same color and arriving sequence. Then we basically decide the maximum length section color time to winner team using stage race method. For the case of the loser team with no more racing or yield to loser team and more longer stage in upcoming racing, the winner team give way to loser team. As a result, all cars(runners) are winner in any stage without fail. For n cars, the proposed algorithm has a advantage of simple and fast with O(nlogn) polynomial time complexity, this algorithm can be get the minimum number of moving distance and purging for all of experimental data.

• Industrial Engineering and Management Systems
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• v.7 no.2
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• pp.171-181
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• 2008

Home care, known also as domiciliary care, is part of the community care service that is a responsibility of the local government authorities in the UK as well as many other countries around the world. The aim is to provide the care and support needed to assist people, particularly older people, people with physical or learning disabilities and people who need assistance due to illness to live as independently as possible in their own homes. It is performed primarily by care workers visiting clients' homes where they provide help with daily activities. This paper is concerned with the dispatching of care workers to clients in an efficient manner. The optimized routine for each care worker determines a schedule to achieve the minimum total cost (in terms of distance traveled) without violating the capacity and time window constraints. A collaborative population-based meta-heuristic called Particle Swarm Optimization (PSO) is applied to solve the problem. A particle is defined as a multi-dimensional point in space which represents the corresponding schedule for care workers and their clients. Each dimension of a particle represents a care activity and the corresponding, allocated care worker. The continuous position value of each dimension determines the care worker to be assigned and also the assignment priority. A heuristic assignment scheme is specially designed to transform the continuous position value to the discrete job schedule. This job schedule represents the potential feasible solution to the problem. The Earliest Start Time Priority with Minimum Distance Assignment (ESTPMDA) technique is developed for generating an initial solution which guides the search direction of the particle. Local improvement procedures (LIP), insertion and swap, are embedded in the PSO algorithm in order to further improve the quality of the solution. The proposed methodology is implemented, tested, and compared with existing solutions for some 'real' problem instances.

• Journal of the Korea Society of Computer and Information
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• v.20 no.7
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• pp.41-47
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• 2015

The bandwidth minimization problem (BMP) has been classified as NP-complete because the polynomial time algorithm to find the optimal solution has been unknown yet. This paper suggests polynomial time heuristic algorithm is to find the solution of bandwidth minimization problem. To find the minimum bandwidth ${\phi}^*=_{min}{\phi}(G)$, ${\phi}(G)=_{max}\{{\mid}f(v_i)-f(v_j):v_i,v_j{\in}E\}$ for given graph G=(V,E), m=|V|,n=|E|, the proposed algorithm sets the maximum degree vertex $v_i$ in graph G into global central point (GCP), and labels the median value ${\lceil}m+1/2{\rceil}$ between [1,m] range. The graph G is partitioned into subgroup, the maximum degree vertex in each subgroup is set to local central point (LCP), and we adjust the label of LCP per each subgroup as possible as minimum distance from GCP. The proposed algorithm requires O(mn) time complexity for label to all of vertices. For various twelve graph, the proposed algorithm can be obtains the same result as known optimal solution. For one graph, the proposed algorithm can be improve on known solution.

• Journal of Korean Institute of Industrial Engineers
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• v.27 no.2
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• pp.140-149
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• 2001

The paper discusses the problem of finding the Origin-Destination(O-D) shortest paths in internodal transportation networks with time-schedule constraints. The shortest path problem on the internodal transportation network is concerned with finding a path with minimum distance, time, or cost from an origin to a destination using all possible transportation modalities. The time-schedule constraint requires that the departure time to travel from a transfer station to another node takes place only at one of pre-specified departure times. The scheduled departure times at the transfer station are the times when the passengers are allowed to leave the station to another node using the relative transportation modality. Therefore, the total time of a path in an internodal transportation network subject to time-schedule constraints includes traveling time and transfer waiting time. In this paper, a genetic algorithm (GA) approach is developed to deal with this problem. The effectiveness of the GA approach is evaluated using several test problems.

• Journal of the Korean Operations Research and Management Science Society
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• v.25 no.4
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• pp.97-109
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• 2000

This work proposes a neural network approach to solve vehicle routing problems which have diverse application areas such as vehicle routing and robot programming. In solving these problems, classical mathematical approaches have many difficulties. In particular, it is almost impossible to implement a real-time vehicle routing with multiple vehicles. Recently, many researchers proposed methods to overcome the limitation by adopting heuristic algorithms, genetic algorithms, neural network techniques and others. The most basic model for path planning is the Travelling Salesman Problem(TSP) for a minimum distance path. We extend this for a problem with dynamic upcoming of new positions with multiple vehicles. In this paper, we propose an algorithm based on SOM(Self-Organization Map) to obtain a sub-optimal solution for a real-time vehicle routing problem. We develope a model of a generalized multiple TSP and suggest and efficient solving procedure.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.4 no.5
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• pp.753-775
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• 2010

Greedy forwarding is a key mechanism of geographic routing using distance as a metric. As greedy forwarding only uses 1-hop neighbor node information, it minimizes routing overhead and is highly scalable. In existing greedy forwarding schemes, a node selects a next forwarding node based only on the distance. However, the signal strength in a realistic environment reduces exponentially depending on the distance, so that by considering only the distance, it may cause a large number of data packet retransmissions. To solve this problem, many greedy forwarding schemes have been proposed. However, they do not consider the unreliable and asymmetric characteristics of wireless links and thus cause the waste of limited battery resources due to the data packet retransmissions. In this paper, we propose a reliable and energy-efficient opportunistic greedy forwarding scheme for unreliable and asymmetric links (GF-UAL). In order to further improve the energy efficiency, GF-UAL opportunistically uses the path that is expected to have the minimum energy consumption among the 1-hop and 2-hop forwarding paths within the radio range. Comprehensive simulation results show that the packet delivery rate and energy efficiency increase up to about 17% and 18%, respectively, compared with the ones in PRR${\times}$Distance greedy forwarding.

• KIEAE Journal
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• v.4 no.4
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• pp.35-44
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• 2004

In Korea, the apartment buildings have been constructed recently in large quantities to provide housing due to the gravitation of population towards large cities. However, because of this trend toward high-rise apartment, a number of problems are occurred such as the deterioration of comfort in the dwelling environment and the lack of solar access right in apartment buildings. In the building law, the sunshine hour and the minimal separated distance between apartment buildings as regulated as the criteria for the site planning. Most of site planners, however, designed the apartment site only following minimum separated distance. As a result, the problem of sunshine hours lack is caused and legal dispute concerning solar access right is also arisen. The purpose of this study is to improve solar access right regulation and to help site design of apartment planning. Accordingly, we execute empirical analysis based on computer simulation in order to find suitable separated distance between typically designed apartment buildings. First, we estimated sunshine hours according to independent building orientation, height, and length. Second, we calculated sunshine hours in various case of apartment arrangement; parallel type, courtyard type, tower type and etc. with various separated distance.

• Communications of the Korean Mathematical Society
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• v.34 no.3
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• pp.1029-1047
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• 2019

We are interested in the problem of determining the best fitted circle to a set of data points in space. This can be usually obtained by minimizing the geometric distances or various approximate algebraic distances from the fitted circle to the given data points. In this paper, we propose an algorithm in such a way that the sum of the squares of the geometric distances is minimized in ${\mathbb{R}}^3$. Our algorithm is mainly based on the steepest descent method with a view of ensuring the convergence of the corresponding objective function Q(u) to a local minimum. Numerical examples are given.