• Journal of the Korean Institute of Intelligent Systems
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• v.24 no.4
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• pp.412-417
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• 2014

This paper presents the development and control of an omni-directional holonomic mobile robot platform, which is equipped with three lateral orthogonal-wheel assemblies. Omni-directionality can be achieved with decoupled rotational and translational motions. Simulation studies on collision avoidance are conducted. A real robot is built and its hardware is implemented to control the robot. Control algorithm is embedded on DSP and FPGA chips. Hardware for motor control such as PWM, encoder counter, serial communication modules is implemented on an FPGA chip. Experimental studies of following joystick commands are performed to demonstrate the functionality and controllability of the robot.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2001.05a
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• pp.136-139
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• 2001

This paper develops a sensor based navigation method that utilizes fuzzy logic and the Dempster-Shafer evidence theory for mobile robot in uncertain environment. The proposed navigator consists of two behaviors: obstacle avoidance and goal seeking. To navigate reliably in the environment, we make a map building process before the robot finds a goal position and create a robust fuzzy controller. In this paper, the map is constructed on a two-dimensional occupancy grid. The sensor readings are fused into the map using D-S inference rule. Whenever the robot moves, it catches new information about the environment and replaces the old map with new one. With that process the robot can go wandering and finding the goal position. The usefulness of the proposed method is verified by a series of simulations. This paper deals with the fuzzy modeling for the complex and uncertain nonlinear systems, in which conventional and mathematical models may fail to give satisfactory results. Finally, we provide numerical examples to evaluate the feasibility and generality of the proposed method in this paper.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2002.10a
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• pp.235-240
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• 2002

This paper presents a new approach to the design of cruise control system of a mobile robot with two drive wheel. The proposed control scheme uses a Gaussian function as a unit function in the fuzzy neural network, and back propagation algorithm to train the fuzzy neural network controller in the framework of the specialized learning architecture. It is proposed a learning controller consisting of two neural network-fuzzy based on independent reasoning and a connection net with fixed weights to simply the neural networks-fuzzy. The performance of the proposed controller is shown by performing the computer simulation for trajectory tracking of the speed and azimuth of a mobile robot driven by two independent wheels.

• Journal of Electrical Engineering and Technology
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• v.9 no.3
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• pp.1034-1042
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• 2014

For the remote operation of the mobile robot, the human operator depends fully on the sensory information which is the partial information of the workspace of the mobile robot. It is usually very hard to fully manually operate the mobile robot in this situation. We propose the efficient guidance navigation method for improving the efficiency of the remote operation with the expanded guide circle using the sensory information. The guidance command is generated from the proposed algorithm using the expanded guide circle. We evaluated the performance of the proposed algorithm using the experiments.

• Journal of the Korean Institute of Intelligent Systems
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• v.26 no.1
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• pp.16-22
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• 2016

The research on two-wheeled inverted pendulum (TWIP) mobile robots has been ongoing in a number of robotic laboratories around the world. In this paper, we consider a robust controller design for the TWIP mobile robot driving on uniform slopes. We use a 2 degree-of-freedom (DOF) model which is obtained by restricting the spinning motion in a 3 DOF motion dynamic equation. In order to design the robust controller guaranteeing stability of the TWIP mobile robot driving on inclined surface, we propose a sliding mode control based on the theory of variable structure systems and design a sliding surface using the theory of the linear quadratic regulation (LQR). For simulation, the dynamic model of the TWIP mobile robot is constructed using Mathworks' Simulink and the sliding mode control is also implemented using Simulink. From simulation results, we show that the proposed controller effectively controls the TWIP mobile robot driving on slopes.

• Journal of the Korean Institute of Intelligent Systems
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• v.22 no.5
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• pp.570-576
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• 2012

In this research a moving platform for a mobile robot which can run with upright posture is proposed. It is able to stand with standing arms and run uprightly based on an inverted pendulum mechanism. Conventional mobile robots generally may equip 4 wheels or 3 wheels including a caster and have good statistic stability. They need a steering mechanism to choose which way to go since they have a square or rectangular configuration with multiple wheels. When a mobile robot meets a sharply perpendicular and narrow crossroad, it may need a special steering scheme such as going forward and backward repeatedly or it sometimes cannot even pass through the crossroad because of its size. The proposed moving platform for a mobile robot changes to a upright posture which has a small planar area and is able to pass through the crossroad. We propose a moving platform for a mobile robot with a inverted pendulum mechanism and standing arms which can make the mobile robot upright.

• Journal of the Korean Institute of Intelligent Systems
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• v.14 no.3
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• pp.304-309
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• 2004

This work proposes the wearable and intelligent system to control mobile vehicle instead of user. The system having the ability of assistance as well as portable can be applied to various controller. It is possible to observe the state of mobile vehicle and have a good command of robot instead of human. In this paper, the wearable system operating the mobile vehicle by deciding the velocity and rotation angle that are demanded for collision avoidance with the obtained driving information from mobile vehicle is implemented. To make the proposed wearable system have an intelligence, the hierarchical fuzzy logic and neural network are used.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.23 no.4
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• pp.374-379
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• 2014

Mobile robots with omni-directional wheels can generate instant omni-directional motion without requiring extra space to change the direction of the body. Therefore, they are capable of moving in an arbitrary direction under any orientation even in narrow aisles or tight areas. In this research, an omni-directional mobile robot based on Mecanum wheels was developed to achieve omni-directionality. A CompactRIO embedded real-time controller and C series motion and I/O modules were employed in the control system design. Ultrasonic sensors installed on the front and lateral sides were utilized to measure the distance between the mobile robot and the side wall of a workspace. Through intensive experiments, a performance evaluation of the mobile robot was conducted to confirm its feasibility for industrial purposes. Mobility, omni-directionality, climbing capacity, and tracking performance of a squared trajectory were selected as performance indices to assess the omni-directional mobile robot.

• Journal of the Korean Institute of Intelligent Systems
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• v.17 no.6
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• pp.744-748
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• 2007

In this paper, a new localization method of indoor mobile robot using multi IR(infrared) range sensors is proposed. Each IR range sensor detects the edge of obstacles and wall using the acquired range data. The environment map is built by the merging process of the detected edge data of each sensor. The performance of proposed system is verified by the comparison of the real environment and the detected map in experiments.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.09a
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• pp.496-499
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• 2003

Support Vector Machines (SVMs) is applied to a practical problem as one of standard tools for machine learning. The application of Reinforcement Learning (RL) and SVMs in action of mobile robot is investigated. A technique to decide the action of autonomous mobile robot in practice is explained in the paper, The proposed method is to find n basis for good action of the system under unknown environment. In multi-dimensional sensor input, the most reasonable action can be automatically decided in each state by RL. Using SVMs, not only optimal decision policy but also generalized state in unknown environment is obtained.