• The KIPS Transactions:PartD
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• v.15D no.3
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• pp.305-312
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• 2008

Web documents with useful information are widely available on the internet and they are accessible with web search service. For this reason, web search services study better ways to collect more web documents, but have a difficulty figuring out the coverage of these web pages. This paper is intended to find ways to evaluate the current coverage assessment methods and suggest more effective coverage assessment technique that is, sampling internet web documents equally, monitoring how they are classified on web search services, in an attempt to assess both absolute and relative coverage of the web search engines. The paper also presents the comparison among Korean web search services using the suggested methods.the absolute and relative coverage was highest in Google followed by Naver and Empas. The result is expected to help estimating coverage of web search services.

• The KIPS Transactions:PartB
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• v.15B no.2
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• pp.159-170
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• 2008

Nowadays, one of the important functionalities required from robot task planners is to generate plans to compose existing component services into a new service. In this paper, we introduce the design and implementation of a heuristic search planner, JPLAN, as a kernel module for component service composition. JPLAN uses a local search algorithm and planning graph heuristics. The local search algorithm, EHC+, is an extended version of the Enforced Hill-Climbing(EHC) which have shown high efficiency applied in state-space planners including FF. It requires some amount of additional local search, but it is expected to reduce overall amount of search to arrive at a goal state and get shorter plans. We also present some effective heuristic extraction methods which are necessarily needed for search on a large state-space. The heuristic extraction methods utilize planning graphs that have been first used for plan generation in Graphplan. We introduce some planning graph heuristics and then analyze their effects on plan generation through experiments.

• Journal of the Korean Institute of Intelligent Systems
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• v.17 no.5
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• pp.591-597
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• 2007

In swarm robot systems, each robot must behaves by itself according to the its states and environments, and if necessary, must cooperates with other robots in order to carry out a given task. Therefore it is essential that each robot has both learning and evolution ability to adapt the dynamic environments. In this paper, the new polygon based Q-learning algorithm and distributed genetic algorithms are proposed for behavior learning and evolution of collective autonomous mobile robots. And by distributed genetic algorithm exchanging the chromosome acquired under different environments by communication each robot can improve its behavior ability Specially, in order to improve the performance of evolution, selective crossover using the characteristic of reinforcement learning is adopted in this paper. we verify the effectiveness of the proposed method by applying it to cooperative search problem.

• Journal of Intelligence and Information Systems
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• v.11 no.1
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• pp.153-167
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• 2005

In this paper, we consider the problem of exploring unknown environments with a mobile robot or an autonomous character agent. Traditionally, research efforts to address the space exploration problem havefocused on the graph-based space representations and the graph search algorithms. Recently EXPLORE, one of the most efficient search algorithms, has been discovered. It traverses at most min$min(mn, d^2+m)$ edges where d is the deficiency of a edges and n is the number of edges and n is the number of vertices. In this paper, we propose DFS-RTA* and DFS-PHA*, two real-time graph search algorithms for directing an autonomous agent to explore in an unknown space. These algorithms are all built upon the simple depth-first search (DFS) like EXPLORE. However, they adopt different real-time shortest path-finding methods for fast backtracking to the latest node, RTA* and PHA*, respectively. Through some experiments using Unreal Tournament, a 3D online game environment, and KGBot, an intelligent character agent, we analyze completeness and efficiency of two algorithms.

• Transactions on Control, Automation and Systems Engineering
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• v.4 no.2
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• pp.176-187
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• 2002

This paper focuses on the study of simulation and evolution of Micro Air Vehicles. Micro Air Vehicles or MAVs are small flying robots that are used for surveillance, search and rescue, and other missions. The simulated robots are designed based on realistic characteristics and the brains (controllers) of the robots are generated using genetic algorithms, i .e., simulated evolution. The objective for the experiments is to investigate the effects of robot team size and topology (simulation environment) on the evolution of simulated robots. The testing of team sizes deals with finding an ideal number of robots to be deployed for a given mission. The goal of the topology experiments is to see if there is an ideal topology (environment) to evolve the robots in order to increase their utility in most environments. We compare the results of the various experiments by evaluating the fitness values of the robots i .e., performance measure. In addition, evolved robot teams are tested in different situation in order to determine if the results can be generalized, and statistical analysis is performed to evaluate the evolved results.

• Journal of the Korean Institute of Intelligent Systems
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• v.17 no.2
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• pp.259-264
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• 2007

In this paper, we composed chaos mobile robot by embedding many type of chaos circuit including Arnold Equation and Chua's Equation and proposed method of evaluation of obstacles when it meets or approaches an obstacle while the mobile robot searches an any plane with chaos trajectory and method of concentrating search when it faces target and verified these results. For obstacles avoidance, we developed algorithm that evades an obstacles with chaos trajectory by assuming fixed obstacle, obstacles using VDP model, hidden obstacles using BVP model as obstacles and for searching an object, we developed algorithm of searching with a chaos trajectory by assuming BVP model as an object, verified the results and confirmed reasonability of them.

• Journal of Mechanical Science and Technology
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• v.18 no.1
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• pp.92-105
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• 2004

A new algorithm for planning a collision-free path based on algebraic curve is developed and the concept of collision-free Path Space (PS) is introduced. This paper presents a Geometry Mapping (GM) based on two straight curves in which the intermediate connection point is organized in elliptic locus ($\delta$, $\theta$). The GM produces two-dimensional PS that is used to create the shortest collision-free path. The elliptic locus of intermediate connection point has a special property in that the total distance between the focus points through a point on ellipse is the same regardless of the location of the intermediate connection point on the ellipse. Since the radial distance, a, represents the total length of the path, the collision-free path can be found as the GM proceeds from $\delta$=0 (the direct path) to $\delta$=$\delta$$\_$max/(the longest path) resulting in the minimum time search. The GM of elliptic workspace (EWS) requires calculation of interference in circumferential direction only. The procedure for GM includes categorization of obstacles to .educe necessary calculation. A GM based on rectangular workspace (RWS) using Cartesian coordinate is also considered to show yet another possible GM. The transformations of PS among Circular Workspace Geometry Mapping (CWS GM) , Elliptic Workspace Geometry Mapping (EWS GM) , and Rectangular Workspace Geometry Mapping (RWS GM), are also considered. The simulations for the EWS GM on various computer systems are carried out to measure performance of algorithm and the results are presented.

• The Journal of Korea Robotics Society
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• v.5 no.3
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• pp.186-196
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• 2010

In this paper, we present a global localization and position error compensation method in a known indoor environment using magnet hall sensors. In previous our researches, it was possible to compensate the pose errors of $x_e$, $y_e$, ${\theta}_e$ correctly on the surface of indoor environment with magnets sets by regularly arrange the magnets sets of identical pattern. To improve the proposed method, new strategy that can realize the global localization by changing arrangement of magnet pole is presented in this paper. Total six patterns of the magnets set form the unique landmarks. Therefore, the virtual map can be built by using the six landmarks randomly. The robots search a pattern of magnets set by rotating, and obtain the current global pose information by comparing the measured neighboring patterns with the map information that is saved in advance. We provide experimental results to show the effectiveness of the proposed method for a differential drive wheeled mobile robot.

• Journal of Institute of Control, Robotics and Systems
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• v.8 no.2
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• pp.142-150
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• 2002

This paper presents a systematic method of the zigzag gait planning for quadruped walking robots. When a robot walks with a zigzag gait, its body is allowed to move from side to side, while the body movement is restricted along a moving direction in conventional continuous gaits. The zigzag movement of the body is effective to improve the gait stability margin. To plan a zigzag gait in a systematic way, the relationship between the center of gravity(COG) and the stability margin is firstly investigated. Then, new geometrical method is introduced to plan a sequence of the body movement which guarantees a maximum stability margin as well as monotonicity along a moving direction. Finally, an optimal swing-leg sequence is chosen for a given arbitrary configuration of the robot. To verify the proposed method, computer simulations have been performed for both cases of a periodic gait and a non-periodic gait.

• Journal of the Korean Institute of Intelligent Systems
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• v.18 no.4
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• pp.555-561
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• 2008

This Paper introduces new approach to develop fast and reliable gaits for quadruped robot using GA(genetic algorithm). Planning gaits for legged robots is a challenging task that requires optimizing parameters in a highly irregular and multidimensional space. Recent approaches have problems to select proper parameters which are not known in advance and optimize more than ten to twenty parameters simultaneously. In our approach, the effects of major gait parameters are analysed and used to guide the search more efficiently. The experiments of Sony AIBO ERS-7 in Webots environment indicate that our approach is able to produce much improved results in fast velocity and reliability.