• 제어로봇시스템학회:학술대회논문집
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• 1995.10a
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• pp.392-395
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• 1995

This paper presents a time efficient method for determining a sequence of locations of a mobile manipulator that facilitates tracking of continuous path in cluttered environment. Given the task trajectory in the form of octree data structure, the algorithm performs characterization of task space and subsequent multistage optimization process to determine task feasible locations of the robot. Firstly, the collision free portion of the trajectory is determined and classified according to uniqueness domains of the inverse kinematics solutions. Then by implementing the extent of task feasible subspace into an optimization criteria, a multistage optimization problem is formulated to determines the task feasible locations of the mobile manipulator. The effectiveness of the proposed method is shown through a simulation study performed for a 3-d.o.f. manipulator with generic kinematic structure.

• Proceedings of the KIEE Conference
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• 2000.07d
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• pp.3245-3247
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• 2000

We present an interactive virtual bronchoscopy method, which uses a tree structure of the objects and physically based camera control model. The proposed method archieves faster response by rendering only visible branches using the tree structure of the bronchus. A collision detection algorithm supplies a convenient and intuitive mechanism for examining the bronchus inner surface while a voiding collisions. We have improved the performances of navigation speed in virtual bronchoscopy.

• Journal of the Korean Society for Railway
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• v.10 no.3 s.40
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• pp.337-342
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• 2007

소형궤도 차량 시스템(PRT: Personal Rapid Transit)은 짧은 거리에 비교적 많은 승객을 수송하기 위하여 매우 짧은 차간 간격을 요구하며 또한 차량 간의 충돌을 피하기 위해서 매우 정확한 차량 속도제어 알고리즘을 필요로 한다. 본 논문에서는 소형궤도차량 시스템의 차량의 충돌회피 알고리즘 개발을 위한 장치의 구성에 대해서 다룬다. 개발 장치는 모의 차량, 중앙제어 시스템, 모의지상설비, 모니터링 장치로 구성되며, 설계된 알고리즘의 모의시험을 위해서 Labview Simulation Interface Toolkit과 Matlab/Simulink가 결합된 모의시험 환경을 이용한다.

• Journal of the Korean Society of Industry Convergence
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• v.22 no.3
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• pp.315-324
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• 2019

This paper present a novel 6-axis robotic base platform with force/moment sensing. The robotic base platform is made up of six loadcells connecting the moving plate to the fixed plate by spherical joints at the both ends of loadcells. The statics relation is derived, the robotic base platform prototype and the loadcell measurement system are developed. The force/moment calibrations in joint and Cartesian spaces are performed. The algorithm to detect external force applied at a working robot is derived, and using a 6-DOF robot mounted on the robotic base platform, force/moment measurement experiments have been performed.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.7 no.7
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• pp.1721-1736
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• 2013

Black and gray hole attack is one kind of routing disturbing attacks and can bring great damage to the network. As a result, an efficient algorithm to detect black and gray attack is important. This paper demonstrate an adaptive approach to detecting black and gray hole attacks in ad hoc network based on a cross layer design. In network layer, we proposed a path-based method to overhear the next hop's action. This scheme does not send out extra control packets and saves the system resources of the detecting node. In MAC layer, a collision rate reporting system is established to estimate dynamic detecting threshold so as to lower the false positive rate under high network overload. We choose DSR protocol to test our algorithm and ns-2 as our simulation tool. Our experiment result verifies our theory: the average detection rate is above 90% and the false positive rate is below 10%. Moreover, the adaptive threshold strategy contributes to decrease the false positive rate.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2012.10a
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• pp.261-262
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• 2012

In this research, intelligent protection system for laser boat is introduced. The function of system is to measure the distance and velocity of object from our boat and generate control signals to avoid collision with moving targets. A novel approach to estimate object's position from our ship is tackled on this paper. To do this laser sensors are used to measure distance from ship to targets. The ship position and velocity is estimated by th Kalman filter algorithm. In the real phase, the filtering method will be applied to process signal gathered by laser sensors. Simulation to estimate ship's position and velocity under noise are executed and the results are introduced to show the effectiveness of the algorithm.

• The Journal of Korea Robotics Society
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• v.19 no.2
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• pp.149-158
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• 2024

This paper presents a robust autonomous navigation and reconnaissance system for tracked robots, designed to handle complex multi-floor indoor environments with stairs. We introduce a localization algorithm that adjusts scan matching parameters to robustly estimate positions and create maps in environments with scarce features, such as narrow rooms and staircases. Our system also features a path planning algorithm that calculates distance costs from surrounding obstacles, integrated with a specialized PID controller tuned to the robot's differential kinematics for collision-free navigation in confined spaces. The perception module leverages multi-image fusion and camera-LiDAR fusion to accurately detect and map the 3D positions of objects around the robot in real time. Through practical tests in real settings, we have verified that our system performs reliably. Based on this reliability, we expect that our research team's autonomous reconnaissance system will be practically utilized in actual disaster situations and environments that are difficult for humans to access, thereby making a significant contribution.

• The Journal of Korea Robotics Society
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• v.19 no.1
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• pp.117-129
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• 2024

This paper introduces collective navigation through a narrow gap using a curriculum-based deep reinforcement learning algorithm for a swarm of unmanned aerial vehicles (UAVs). Collective navigation in complex environments is essential for various applications such as search and rescue, environment monitoring and military tasks operations. Conventional methods, which are easily interpretable from an engineering perspective, divide the navigation tasks into mapping, planning, and control; however, they struggle with increased latency and unmodeled environmental factors. Recently, learning-based methods have addressed these problems by employing the end-to-end framework with neural networks. Nonetheless, most existing learning-based approaches face challenges in complex scenarios particularly for navigating through a narrow gap or when a leader or informed UAV is unavailable. Our approach uses the information of a certain number of nearest neighboring UAVs and incorporates a task-specific curriculum to reduce learning time and train a robust model. The effectiveness of the proposed algorithm is verified through an ablation study and quantitative metrics. Simulation results demonstrate that our approach outperforms existing methods.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.13 no.8
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• pp.1483-1490
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• 2009

This parer is presented multiple path-planning of unmanned autonomous forklift using modified genetic algorithm and fuzzy inference system. There are a task-level feedback method and a method that path is dynamically replaned in realtime while the autonomous vehicles are moving by means of an optimal algorithm for existing multiple path-planning. However, such methods cause malfunctions and inefficiency in the sense of time and energy, and path-planning should be dynamically replanned in realtime. To solve these problems, we propose multiple path-planning using modified genetic algorithm and fuzzy inference system and show the performance with autonomous vehicles. For experiment, we designed and built two autonomous mobile vehicles that equipped with the same driving control part used in actual autonomous forklift, and test the proposed multiple path-planning algorithm. Experimental result that actual autonomous mobile vehicle, we verified that fast optimized path-planning and efficient collision avoidance are possible.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.37 no.7C
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• pp.544-553
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• 2012

A vehicle localization technology is an essential component to support many smart-vehicle applications, e.g. collision warning, adaptive cruise control, and so on. In this paper, we present a new vehicle localization algorithm based on the fusion of the sensing estimates from the local sensors and the GPS estimates from the inter-vehicle communications. The proposed algorithm consists of the greedy location data mapping algorithm and the position refinement algorithm. The former maps a sensing estimate with a GPS estimate based on the distance between themselves, and then the latter refines the GPS estimate of the subject vehicle based on the law of large numbers. From the numerical results, we demonstrate that the accuracy of the proposed algorithm outperforms that of the existing GPS estimates by at least 30 % in the longitudinal direction and by at least 60% in the lateral direction.