• 제어로봇시스템학회:학술대회논문집
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• 2002.10a
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• pp.38.5-38
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• 2002

1. Introduction 2. System Configuration 3. Performance Evaluation 4. conclusion 5. Acknowledgment

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.336-336
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• 2000

This paper studied on the yaw motion of the gantry crane which is used for the automated container terminal. Though several problems are occurred in driving of gantry crane, they are solved by the motion by the operator. But if the gantry crane is unmanned, it is automatically controlled without any human operation. There are two types, cone and flat typo in driving wheel shape. In cone type, lateral vibration and yaw motion of crane are issued. To bring a solution to these problems, the dynamic equation of the gantry crane driving mechanism is derived and it used PD(Proportional-Derivative) controller to control the lateral vibration. The simulation result of the driving mechanism using the Runge-Kutta method is presented in this paper.

• Journal of Institute of Control, Robotics and Systems
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• v.18 no.4
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• pp.359-364
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• 2012

Real-Time Traversability should be analyzed from the equiped sensors' data in real time for autonomous outdoor navigation. However, it is difficult to find out such traversability that considers the terrain roughness and the vehicle dynamics especially in case of skid type vehicle. The traversability based on real time dynamic analysis was proposed to solve such problem but in navigation with strait driving path. To adapt the method into the navigation with curved driving path, a path following controller should be incorporated into the dynamic model even though it cause the real time problem. In this paper, a dynamic model is proposed to solve the real time problem in the traversability analysis based on real time dynamic simualtion. The dynamic model contains the control dummy which is connected to the vehicle body with a universal joint to follow the curved path without controller. Simulation and experimental results on $6{\times}6$ articulated unmanned ground vehicle demonstrate the method's effectiveness and applicability into the traversability analysis on terrain with bumps.

• Journal of Institute of Control, Robotics and Systems
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• v.20 no.9
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• pp.950-956
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• 2014

In this paper, the real-time detection method of a DDP (Driving Direction Point) is proposed for an unmanned vehicle to safely follow the center of the road. Since the DDP is defined as a center point between two lanes, the lane is first detected using a web camera. For robust detection of the lane, the binary thresholding and the labeling methods are applied to the color camera image as image preprocessing. From the preprocessed image, the lane is detected, taking the intrinsic characteristics of the lane such as width into consideration. If both lanes are detected, the DDP can be directly obtained from the preprocessed image. However, if one lane is detected, the DDP is obtained from the inverse perspective image to guarantee reliability. To verify the proposed method, several experiments to detect the DDPs are carried out using a 4 wheeled vehicle ERP-42 with a web camera.

• Journal of the Korean Institute of Intelligent Systems
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• v.18 no.6
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• pp.775-780
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• 2008

In this paper, unmanned driving of robotic vehicle using magnetic marker is proposed. One of the most important component of autonomous vehicle is to detect the position of a magnetic marker on the road. In order to calculate the precise position of a magnet embedded on the road, the relation of magnetic field and a sensor is analyzed, and a new position sensing system using arrayed magnetic sensor is proposed. Also, the steering control system using a stepping motor is developed for driving by automatic mode as well as manual mode. For the verification of usability, the developed robotic vehicle is tested on magnetic road.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.49 no.5
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• pp.249-256
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• 2000

This paper deals with the control of the lateral motion of a mobile vehicle. A mobile vehicle using in this experiment is able to adapt many unmanned automatic driving system, for example, like a automated product transporting system. This vehicle is consist of the two servomotors. One is used to accelerate this vehicle and the another is used to change this lateral direction. An adaptive fuzzy logic controller(AFLC) is designed and applied to a experimental mobile vehicle in order to achieve the control of the lateral direction. An adaptive fuzzy logic controller(AFLC) is designed and applied to a experimental mobile vehicle in order to achieve the control of the lateral motion of the vehicle. Therefore, the main aim of this paper is investigate the possibility of applying adaptive fuzzy control algorithms to a microprocessor-based servomotor controller which requires faster and more accurate response compared with many other industrial processes. Fuzzy control rules are derived by modelling an expert's driving actions. Experiments are performed using a mobile vehicle with sensing units, a microprocessor and a host computer.

• Journal of the Korea Institute of Military Science and Technology
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• v.14 no.6
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• pp.965-973
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• 2011

Small unmanned ground vehicles(SUGVs) are typically operational on unstructured environments such as crashed building, mountain area, caves, and so on. On those terrains, driving control can suffer from the unexpected ground disturbances which occasionally lead turnover situation. In this paper, we have proposed an algorithm which sustains driving stability of a SUGV as preventing from turnover. The algorithm exploits potential field method in order to determine the stability of the robot. Then, the flipper and manipulator posture of the SUGV is optimized from local optimization algorithm known as gradient descent method. The proposed algorithm is verified using 3D dynamic simulation, and results showed that the proposed algorithm contributes to driving stability of SUGV.

• The Journal of Korea Robotics Society
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• v.14 no.4
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• pp.278-284
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• 2019

Legged locomotion has high mobility on irregular surfaces by touching the ground at discrete points. Inspired by the creature's legged locomotion, legged robots have been developed to explore unstructured environments. In this paper, we propose a modular crawler that can easily adjust the number of legs for adapting the environment that the robot should move. One module has a pair of legs, so the number of legs can be adjusted by changing the number of modules. All legs are driven by a single driving motor for simple and compact design, so the driving axle of each module is connected by the universal joint. Universal joints between modules enable the body flexion for steering or overcoming higher obstacles. A prototype of crawler with three modules is built and the driving performance and the effect of module lifting on the ability to overcome obstacles are demonstrated by the experiments.

• Journal of Information Processing Systems
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• v.14 no.6
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• pp.1445-1456
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• 2018

Environment perception and three-dimensional (3D) reconstruction tasks are used to provide unmanned ground vehicle (UGV) with driving awareness interfaces. The speed of obstacle segmentation and surrounding terrain reconstruction crucially influences decision making in UGVs. To increase the processing speed of environment information analysis, we develop a CPU-GPU hybrid system of automatic environment perception and 3D terrain reconstruction based on the integration of multiple sensors. The system consists of three functional modules, namely, multi-sensor data collection and pre-processing, environment perception, and 3D reconstruction. To integrate individual datasets collected from different sensors, the pre-processing function registers the sensed LiDAR (light detection and ranging) point clouds, video sequences, and motion information into a global terrain model after filtering redundant and noise data according to the redundancy removal principle. In the environment perception module, the registered discrete points are clustered into ground surface and individual objects by using a ground segmentation method and a connected component labeling algorithm. The estimated ground surface and non-ground objects indicate the terrain to be traversed and obstacles in the environment, thus creating driving awareness. The 3D reconstruction module calibrates the projection matrix between the mounted LiDAR and cameras to map the local point clouds onto the captured video images. Texture meshes and color particle models are used to reconstruct the ground surface and objects of the 3D terrain model, respectively. To accelerate the proposed system, we apply the GPU parallel computation method to implement the applied computer graphics and image processing algorithms in parallel.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2016.10a
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• pp.224-226
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• 2016

With the technical development and rapid increase of private demand, the new market for unmanned vehicle combined with the characteristics of 'unmanned automation' and 'vehicle' is rapidly growing. Even though the pilot driving is currently allowed in some countries, there is no country that has institutionalized the formal driving of self-driving cars. In case of the existing vehicles, safety incidents are frequently happening due to the frequent malfunction of the rear sensor, blind spot of the rear camera, or drivers' carelessness. Once such minor flaws are complemented, the relevant regulations for the commercialization of self-driving car and small drone could be relieved. Contrary to the ultrasonic and laser sensors used for the existing vehicles, this paper aims to attempt the distance measurement by using the depth sensor. A depth camera calculates the distance data based on the TOF method calculating the time difference by lighting laser or infrared light onto an object or area and then receiving the beam coming back. As this camera can obtain the depth data in the pixel unit of CCD camera, it can be used for collecting depth data in real-time. This paper suggests to solve problems mentioned above by using depth data in real-time and also to design the obstacle avoidance system through distance measurement.