• The Journal of the Korea institute of electronic communication sciences
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• v.4 no.1
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• pp.37-45
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• 2009

The power wheelchair is an important and convenient mobility device. The demand of power wheelchair is increasing for assistance in mobility. In this paper we proposed a robotic wheelchair for mobility aid to reduce the burden from the disabled. The main issue in an autonomous wheelchair is the automatic detection and avoidance of obstacles and going to the pre-designated place. The proposed algorithm detects the obstacles and avoids them to drive the wheelchair to the desired place safely with panning scan from sensors of distance measurement and fuzzy control. By this way, the disabled will not always have to worry about paying deep attention to the surroundings and his path.

• Journal of information and communication convergence engineering
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• v.4 no.1
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• pp.28-35
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• 2006

In this paper, we propose to synchronization method for mutual cooperative control method that have unstable limit cycles in a chaos trajectory surface in the chaotic UAVs. We assume all obstacles in the chaos trajectory surface have a Van der Pol equation with an unstable limit cycle. We also show computer simulation results of Arnold equation, Chua's equation trajectories with one or more Van der Pol as a obstacles. We proposed and verified the results of the method to make the embedding chaotic UAV to synchronization with the chaotic trajectory in any plane.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.6 no.4
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• pp.314-320
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• 2006

Navigation in environments that are densely cluttered with obstacles is still a challenge for Autonomous Ground Vehicles (AGVs), especially when the configuration of obstacles is not known a priori. Reactive local navigation schemes that tightly couple the robot actions to the sensor information have proved to be effective in these environments, and because of the environmental uncertainties, STSF(Space and Time Sensor Fusion)-based fuzzy behavior systems have been proposed. Realization of autonomous behavior in mobile robots, using STSF control based on spatial data fusion, requires formulation of rules which are collectively responsible for necessary levels of intelligence. This collection of rules can be conveniently decomposed and efficiently implemented as a hierarchy of fuzzy-behaviors. This paper describes how this can be done using a behavior-based architecture. The approach is motivated by ethological models which suggest hierarchical organizations of behavior. Experimental results show that the proposed method can smoothly and effectively guide a robot through cluttered environments such as dense forests.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.63 no.1
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• pp.108-114
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• 2014

This paper proposes a generation method of a navigation guidance direction and a fuzzy controller to achieve the autonomous navigation of a mobile robot using a particle swarm optimization(PSO) scheme in unknown environments. The proposed navigation guidance direction is the direction that leads a mobile robot to arrive a target point simultaneously with avoiding obstacles efficiently according to the surrounding local informations. It is generated by selecting the most suitable direction of the many directions in the surrounding environment using a particle swarm optimization scheme. Also, a robot can reach a target point with avoiding the various obstacles by controlling the robot so that it can move from its current orientation to the navigation guidance direction using the proposed fuzzy controller. Simulation results are presented to show the feasibility and validity of the proposed robot navigation scheme.

• International Journal of Control, Automation, and Systems
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• v.4 no.4
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• pp.395-404
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• 2006

The objective of this paper is to generate a desired flight path to be followed by an autonomous airship. The space is supposed without obstacles. As there are six degrees of freedom and only three inputs for the LSC AS200 airship, three equality constraints appear due to the under-actuation.

• 제어로봇시스템학회:학술대회논문집
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• 2002.10a
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• pp.76.5-76
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• 2002

In this paper, we provide a Nash solution to predictive control problem for nonminimum phase singular nonlinear systems. Until now, there is no result on predictive control problem for this class of nonlinear systems. Chen's recent work considered predictive control problem for a class of nonlinear systems with ill-defined relative degree. Since his work is not a result considered in the feedback linearization framework, there is no a result on singular probem in his paper. In contrast to the existing predictive control result, our work considers two main obstacles (singularity and nonminimum phase) in the feedback linearization framework. For a generally formu...

• Korean Journal of Computational Design and Engineering
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• v.20 no.3
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• pp.298-311
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• 2015

Nowadays, automatic digging operation of an excavator is a big challenge due to the complexity of digging environment, the hardness of soil and buried obstacles into the ground. In order to achieve the maximum soil bucket volume, this paper introduces a novel engineering model that was developed as a virtual excavator in the design phase. Through this model, the designs of mechanical and control systems for autonomous excavator are executed and modified easily before developing in real testbed. Based on a concept of an autonomous excavation, a mechanical system of excavator was first designed in SOLIDWORKS, and a soil model also was modeled by finite-element analysis in ANSYS, both modeled models were then exported to ADAMS environment to investigate the digging behavior through virtual simulation. An intelligent control strategy was generated in MATLAB/Simulink to control the excavator operation. The simulation results were demonstrated by effectiveness of the proposed excavator robot in testing scenarios with many soil types and obstacles.

• 제어로봇시스템학회:학술대회논문집
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• 1989.10a
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• pp.643-648
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• 1989

This paper presents a theoretic study and computer simulation of models and approaches for dynamical obstacle avoidance by mobile robots. The movement of obstacles in unknown environment is described by any one or a combination of three models. The control strategy of the mobile robots is formulated based on one of three approaches. A trajectory-guided control strategy for dynamical obstacle avoidance has been developed. The method greatly simplifies the control process of mobile robots, and is computationally attractive.

• The Journal of Korea Robotics Society
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• v.12 no.3
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• pp.297-305
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• 2017

As the development of autonomous vehicles becomes realistic, many automobile manufacturers and components producers aim to develop 'completely autonomous driving'. ADAS (Advanced Driver Assistance Systems) which has been applied in automobile recently, supports the driver in controlling lane maintenance, speed and direction in a single lane based on limited road environment. Although technologies of obstacles avoidance on the obstacle environment have been developed, they concentrates on simple obstacle avoidances, not considering the control of the actual vehicle in the real situation which makes drivers feel unsafe from the sudden change of the wheel and the speed of the vehicle. In order to develop the 'completely autonomous driving' automobile which perceives the surrounding environment by itself and operates, ability of the vehicle should be enhanced in a way human driver does. In this sense, this paper intends to establish a strategy with which autonomous vehicles behave human-friendly based on vehicle dynamics through the reinforcement learning that is based on Q-learning, a type of machine learning. The obstacle avoidance reinforcement learning proceeded in 5 simulations. The reward rule has been set in the experiment so that the car can learn by itself with recurring events, allowing the experiment to have the similar environment to the one when humans drive. Driving Simulator has been used to verify results of the reinforcement learning. The ultimate goal of this study is to enable autonomous vehicles avoid obstacles in a human-friendly way when obstacles appear in their sight, using controlling methods that have previously been learned in various conditions through the reinforcement learning.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.815-818
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• 1997

In this paper, an intelligent control strategy for a mobile vehicle, based on the technology of the artificial neural network in a Neurocomputer, is presented. The mobile vehicle learned recognizing and driving knowledge by a neurocomputer. Moment Invariants computation was used to extract the shape of objects. The technologies of both neurocomputer and Neumann-type computer are applied into the control system, and make the mobile vehicle be capable of tracking designated objects and avoiding obstacles.