• International Journal of Aeronautical and Space Sciences
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• v.18 no.4
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• pp.740-756
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• 2017

This paper proposes a feedback-linearization-based control algorithm for multirotor unmanned aerial vehicles (UAVs). The feedback linearization scheme is highly efficient for considering nonlinearity between the rotational and translational motion of multirotor UAVs. We also propose a dynamic equation that reflects the aerodynamic effects of the vehicles; the equation's parameters can be determined through curve fitting using actual flight data. We derive the feedback linearization controller from the proposed dynamic equation, and propose a Luenberger observer to attenuate measurement noises. The proposed algorithm is implemented using our in-house flight control computer, and we describe its implementation in detail. To investigate the performance of the proposed algorithm, we carry out two flight scenarios: the first scenario, an autonomous landing on a moving platform, is a test of maneuverability; the second, picking up and replacing an object, test the algorithm's accuracy. In these scenarios, the proposed algorithm precisely controls multirotor UAVs, and we confirm that it can be successfully applied to real flight environments.

• Journal of Institute of Control, Robotics and Systems
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• v.19 no.7
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• pp.614-619
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• 2013

In multi-robot systems, ultrasonic sensors are widely used for localization and/or obstacle detection. However, conventional ultrasonic sensors have a drawback, that is, the interference problem among ultrasonic transmitters. There are some previous studies to avoid interferences, such as TDMA (Time Division Multiple Access) and CDMA (Code Division Multiple Access). In multiple autonomous mobile robots systems, the Doppler-effect has to be considered because ultrasonic transceivers are attached to the moving robots. To overcome this problem, we find out the ASK (Amplitude Shift Keying)-CDMA technique is more robust to the Doppler-effect than the BPSK (Binary Phase Shift Keying)-CDMA technique. In this paper, we propose a new code-expression method and a Monte-Carlo based algorithm that optimizes the ultrasonic code combination in the ASK-CDMA ultrasonic system. The experimental results show that the proposed algorithm improves the performance of the ultrasonic multiple accessing capacity in the ASK-CDMA ultrasonic system.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.6 no.2
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• pp.161-166
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• 2006

Localization of mobile agent within a sensing network is a fundamental requirement for many applications, using networked navigating systems such as the sonar-sensing system or the visual-sensing system. To fully utilize the strengths of both the sonar and visual sensing systems, This paper describes a networked sensor-based navigation method in an indoor environment for an autonomous mobile robot which can navigate and avoid obstacle. In this method, the self-localization of the robot is done with a model-based vision system using networked sensors, and nonstop navigation is realized by a Kalman filter-based STSF(Space and Time Sensor Fusion) method. Stationary obstacles and moving obstacles are avoided with networked sensor data such as CCD camera and sonar ring. We will report on experiments in a hallway using the Pioneer-DX robot. In addition to that, the localization has inevitable uncertainties in the features and in the robot position estimation. Kalman filter scheme is used for the estimation of the mobile robot localization. And Extensive experiments with a robot and a sensor network confirm the validity of the approach.

• International Journal of Advanced Culture Technology
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• v.4 no.4
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• pp.81-86
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• 2016

In this paper, we propose a simple and effective vision-based tracking controller design for autonomous object tracking using multicopter. The multicopter based automatic tracking system usually unstable when the object moved so the tracking process can't define the object position location exactly that means when the object moves, the system can't track object suddenly along to the direction of objects movement. The system will always looking for the object from the first point or its home position. In this paper, PID control used to improve the stability of tracking system, so that the result object tracking became more stable than before, it can be seen from error of tracking. A computer vision and control strategy is applied to detect a diverse set of moving objects on Raspberry Pi based platform and Software defined PID controller design to control Yaw, Throttle, Pitch of the multicopter in real time. Finally based series of experiment results and concluded that the PID control make the tracking system become more stable in real time.

• Journal of Sensor Science and Technology
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• v.14 no.6
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• pp.409-414
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• 2005

For an intelligent vehicle driving which uses magnetic markers and magnetic sensors, we can get every kind of road information while moving the vehicle if we use the code that is encoded with N, S pole direction of markers. If we make it an only aim to move the vehicle, it becomes easy to control the vehicle the more we put markers close. By the way, to recognize the direction of a marker pole it is much better that the markers have no effect each other. To get road informations and move the vehicle autonomously we propose the methods of arranging magnetic sensors and algorithm of recognizing the position of the vehicle with those sensors. We verified the effectiveness of the methods with computer simulation.

• Journal of the Korean Institute of Intelligent Systems
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• v.22 no.2
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• pp.161-167
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• 2012

This research deals with generating via points for autonomous navigation on a roadway for unmanned vehicles. When a vehicle plans a path from a starting point to a goal point, it should be able to map out which lane on which road it passes by. For this purpose, we should organize positional information of roads and save it as a database. This paper presents methods to save the database and to plan a shortest path to the goal by generating via points in consideration of the moving direction and the lane directions. Then we prove that the proposed algorithm can find the optimal path on the road through simulations.

• Journal of Institute of Control, Robotics and Systems
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• v.14 no.3
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• pp.236-241
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• 2008

This paper presents a method to recognize the plane regions for movement of walking robots. When the autonomous agencies using stereo camera or laser scanning sensor is under unknown 3D environment, the mobile agency has to detect the plane regions to decide the moving direction and perform the given tasks. In this paper, we propose a very fast method for plane detection using normal vector of a triangle by 3 vertices defined on a small circular region. To reduce the effect of noises and outliers, the triangle rotates with respect to the center position of the circular region and generates a series of triangles with different normal vectors based on different three points on the boundary of the circular region. The vectors for several triangles are normalized and then median direction of the normal vectors is used to test the planarity of the circular region. The method is very fast and we prove the performance of algorithm for real range data obtained from a stereo camera system.

• Proceedings of the IEEK Conference
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• 2002.07c
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• pp.1499-1502
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• 2002

A method for acquiring an environmental map by integrating distance data obtained by sonars of a moving robot with web interface is proposed. Sonar data contains outliers in some cases such as ultrasonic beam is projected onto a corner of an object. Therefore, the influence of the outliers should be reduced by detecting outliers. In our method, the outliers are detected by two ways: (i) a method considering geometrical .elation among the observed surface and the projected ultrasonic beau, and (ii) a method considering consistency with data obtained by other sonars. By measurement by the sonar, the distance from the sonar to the obstacle is obtained. Assuming the two dimensional space we can know that the inside of the sector, whose renter coincide with the sonar and whose radius is equal to the obtained distance, is the free area, and a part of the arc of this sector is the obstacle area. The generation of the environmental map is done by integrating the free area and the obstacle area obtained by each measurement by the sonars. Before the integration, the outliers detection is done by two ways mentioned above. Experimental results show that obtained maps obtained by our methods with outliers defection are much better than those by a method without outliers detection.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.45 no.3
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• pp.1-10
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• 2008

An occupancy map is a representation methodology describing the region occupied by objects in 3D space, which can be utilized for autonomous navigation and object recognition. In this paper, we describe a technique for building an occupancy map using depth data extracted from stereo images. In addition, some techniques are proposed for utilizing the occupancy map for the segmentation of object regions. After the geometric information on the ground plane is extracted from a disparity image, the occupancy map is constructed by projecting each matched point to the ground plane-based 3D space. We explain techniques for extracting moving object regions using the occupancy map and present experimental results using real stereo images.

• Journal of Convergence for Information Technology
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• v.7 no.6
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• pp.229-235
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• 2017

In recent years, research has been actively carried out to recognize and recognize objects based on a large amount of data. In this paper, we propose a system that extracts objects that are thought to be obstacles in road driving images and recognizes them by car, man, and motorcycle. The objects were extracted using Optical Flow in consideration of the direction and size of the moving objects. The extracted objects were recognized using Alexnet, one of CNN (Convolutional Neural Network) recognition models. For the experiment, various images on the road were collected and experimented with black box. The result of the experiment showed that the object extraction accuracy was 92% and the object recognition accuracy was 96%.