• The Transactions of the Korean Institute of Electrical Engineers
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• v.39 no.1
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• pp.92-106
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• 1990

Active compliance is often used in the control of robot manipulators for the implementation of complex tasks such as assembly, multi-finger fine motion, legged-vehicle adaptive control,etc. This technique balances the interactive force between the manipulator tip and its working environment with its position and velocity errors to achieve the operation of a damped spring. This paper investigates the effecft of passive compliance on system stability with regard to force feedback implementation for actively compliant motion. Usually it is understood that accurate position control require a stiff system. However, theoretical examination of control experiments on a legged suspension vehicle suggests that, if the control includes discrete-time force feedback, some passive compliance is necessssary at the legs of the vehicle for system stability. This can be an important factor to bl considered in manipulator design and control. A theoretical analysis, numerical simulation, and experimental result, confirming the above conclusion, are introduced in this paper.

• IEMEK Journal of Embedded Systems and Applications
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• v.18 no.2
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• pp.81-87
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• 2023

With the development of BCI devices, it is now possible to use EEG control technology to move the robot's arms or legs to help with daily life. In this paper, we propose a customized vehicle control model based on BCI. This is a model that collects BCI-based driver EEG signals, determines information according to EEG signal analysis, and then controls the direction of the vehicle based on the determinated information through EEG signal analysis. In this case, in the process of analyzing noisy EEG signals, controlling direction is supplemented by using a camera-based eye tracking method to increase the accuracy of recognized direction . By synthesizing the EEG signal that recognized the direction to be controlled and the result of eye tracking, the vehicle was controlled in five directions: left turn, right turn, forward, backward, and stop. In experimental result, the accuracy of direction recognition of our proposed model is about 75% or higher.

• Journal of the Korean Institute of Intelligent Systems
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• v.25 no.3
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• pp.266-271
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• 2015

This paper presents the driving and bouncing control of a robotic vehicle for entertainment and transportation. The robotic vehicle is aimed to carry two passengers with a balancing mechanism by two wheels. To maximize the entertaining purpose, not only the balancing control performance but the bouncing control performance is implemented. Passengers can select different driving modes such as regular driving mode, balancing mode, and bouncing mode. Experimental studies of the balancing control performance as well as the bouncing control performance are conducted to see the feasibility as an entertainment robotic vehicle.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2000.11a
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• pp.207-217
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• 2000

In this paper, we describe an image processing algorithm which is able to recognize the road lane. This algorithm performs to recognize the interrelation between AGV and the other vehicle. We experimented on AGV driving test with color CCD camera which is setup on the top of vehicle and acquires the digital signal. This paper is composed of two parts. One is image preprocessing part to measure the condition of the lane and vehicle. This finds the information of lines using RGB ratio cutting algorithm, the edge detection and Hough transform. The other obtains the situation of other vehicles using the image processing and viewport. At first, 2 dimension image information derived from vision sensor is interpreted to the 3 dimension information by the angle and position of the CCD camera. Through these processes, if vehicle knows the driving conditions which are angle, distance error and real position of other vehicles, we should calculate the reference steering angle.

• Journal of Institute of Control, Robotics and Systems
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• v.21 no.3
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• pp.243-249
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• 2015

In direct vehicle teleoperation, a human operator drives a vehicle at a distance through a pair of master and slave device. However, if there is time delay, it is difficult to remotely drive the vehicle due to slow response. In order to address this problem, we introduced a novel methodology of shared vehicle teleoperation using a virtual driving interface. The methodology was developed with four components: 1) virtual driving environment, 2) interface for virtual driving environment, 3) path generator based on virtual driving trajectory, 4) path following controller. Experimental results showed the effectiveness of the proposed approach in simple and cluttered driving environment as well. In the experiments, we compared two sampling methods, fixed sampling time and user defined instant, and finally merged method showed best remote driving performance in term of completion time and number of collision.

• Journal of the Korean Society for Precision Engineering
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• v.26 no.6
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• pp.74-80
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• 2009

Path tracking of unmanned vehicle is a basis of autonomous driving and navigation. For the path tracking, it is very important to find the exact position of a vehicle. GPS is used to get the position of vehicle and a direction sensor and a velocity sensor is used to compensate the position error of GPS. To detect path lines in a road image, the bird's eye view transform is employed, which makes it easy to design a lateral control algorithm simply than from the perspective view of image. Because the driving speed of vehicle should be decreased at a curved lane and crossroads, so we suggest the speed control algorithm used GPS and image data. The control algorithm is simulated and experimented from the basis of expert driver's knowledge data. In the experiments, the results show that bird's eye view transform are good for the steering control and a speed control algorithm also shows a stability in real driving.

• Journal of Ocean Engineering and Technology
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• v.29 no.5
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• pp.386-391
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• 2015

The mobility of tracked vehicles is mainly influenced by the interaction between the tracks and soil. When the track of a tracked vehicle rotates, there will be a slip effect between the track and the soil, which creates a track shear force and the vehicle’s driving force. In this paper, the modeling of a working tool such as a trenching cutter and a tracked vehicle that is the lower frame of a track-based operating robot was performed. In addition, a numerical simulation was executed to verify the performance of the design objectives and the motion characteristics of the combined system.

• Journal of Biosystems Engineering
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• v.27 no.5
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• pp.409-416
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• 2002

This study was conducted to develop a vehicle, leading or following a worker at a certain distance to assist laborious transporting works in greenhouses. A prototype vehicle, which consisted of the rear driving, the front steering and the console units, was designed and tested in the ideal indoor conditions. Results of this study were summarized as following: 1. The driving unit was designed to travel at the speed ranges of 0.3∼0.8 m/sec depending on the operating modes with a maximum payload of 100 kg. 2. The console unit consisted of a main-board including a 80C196KC microprocessor and peripheral devices, a power-board and safety interlock. Worker-leading, and following modes were available in automatic and manual modes. 3. Steering was achieved by turning the steering motor against the sensed direction. Proper steering angles for correcting travel direction were determined as 5 and 9 degrees when sensing cultivation beds and plants, respectively.

• Journal of Positioning, Navigation, and Timing
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• v.11 no.3
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• pp.209-215
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• 2022

As intelligent autonomous driving vehicle development has become a big topic around the world, accurate reference dynamics estimation has been more important than before. Current systems generally use speed and heading information sensed from a distant point as a vehicle reference dynamic, however, the dynamics between different points are not same especially during rotating motions. In order to estimate properly estimate the reference dynamics from the information such as velocity and heading sensed at a point distant from the reference point such as center of gravity, this study proposes estimating reference dynamics from any location in the vehicle by combining the Bicycle and Ackermann models. A test system was constructed by implementing multiple GNSS/INS equipment on an Robot Operating System (ROS) and an actual car. Angle and speed errors of 10° and 0.2 m/s have been reduced to 0.2° and 0.06 m/s after applying the suggested method.

• Journal of Institute of Control, Robotics and Systems
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• v.15 no.7
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• pp.700-710
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• 2009

A computer vision technique of estimating the location of an unmanned ground vehicle is proposed. Identifying the location of the unmaned vehicle is very important task for automatic navigation of the vehicle. Conventional positioning sensors may fail to work properly in some real situations due to internal and external interferences. Given a DSM(Digital Surface Map), location of the vehicle can be estimated by the registration of the DSM and multiview range images obtained at the vehicle. Registration of the DSM and range images yields the 3D transformation from the coordinates of the range sensor to the reference coordinates of the DSM. To estimate the vehicle position, we first register a range image to the DSM coarsely and then refine the result. For coarse registration, we employ a fast random sample matching method. After the initial position is estimated and refined, all subsequent range images are registered by applying a pair-wise registration technique between range images. To reduce the accumulation error of pair-wise registration, we periodically refine the registration between range images and the DSM. Virtual environment is established to perform several experiments using a virtual vehicle. Range images are created based on the DSM by modeling a real 3D sensor. The vehicle moves along three different path while acquiring range images. Experimental results show that registration error is about under 1.3m in average.