• Proceedings of the KIEE Conference
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• 2003.11b
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• pp.19-22
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• 2003

This paper proposes an algorithm for navigation of an autonomous mobile robot with vision sensor. For obstacle avoidance, we used a curvature trajectory method. Using this method, translational and rotational speeds are controlled independently and the mobile robot traces a smooth curvature trajectory that consists of circle trajectories to a target point. While trying to avoid obstacles, the robot fan be goal-directed using curvature trajectory.

• Proceedings of the KIEE Conference
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• 1999.11c
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• pp.655-657
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• 1999

In this paper we will introduce an posture error reduction algorithm for Mobile Robot. We classified odometry error into two categories. and focus on systematic odometry error correction only. Because it is the primary reason for mobile robot navigation. For this procedure we used some robot specifications and modeled robot behavior. Through some experiment, we could obtain new system specs. After modeling, Robot navigation precision was improved.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.1456-1459
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• 2005

To compensate the drawbacks, a new localization method that estimates the global position of the mobile robot by using a camera set on ceiling in the corridor is proposed. This scheme is not a relative localization, which decreases the position error through algorithms with noisy sensor data. The effectiveness of the proposed localization scheme is demonstrated by the experiments.

• Proceedings of the IEEK Conference
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• 2008.06a
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• pp.673-674
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• 2008

This paper presents a new navigation algorithm of an autonomous mobile robot with vision and IR sensors, Zigbee Sensor Network using fuzzy rules. We also show that the developed mobile robot with the proposed algorithm is navigating very well in complex unknown environments.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1993.06a
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• pp.1258-1261
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• 1993

This paper presents a methodology of path planning and navigation for an autonomous mobile robot. A fast algorithm using decomposition technique, which computes the optimal paths between all pairs of nodes, is proposed for real-time calculation. The robot is controlled by fuzzy approximation reasoning. Our new methodology has been implemented on a mobile robot. The results show that the robot successfully navigates to its destination following the optimal path.

• Journal of IKEEE
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• v.22 no.2
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• pp.440-445
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• 2018

The work presented in this paper deals with a navigation problem for a multiple mobile robots in unknown dynamic environments. The environments are completely unknown to the robots; thus, proximity sensors installed on the robots' bodies must be used to detect information about the surroundings. In order to guide the robots along collision-free paths to reach their goal positions, a navigation method based on a combination of primary strategies has been developed. Most of these strategies are achieved by means of fuzzy logic controllers, and are uniformly applied in every robot. In order to improve the performance of the proposed fuzzy logic, the genetic algorithms were used to evolve the membership functions and rules set of the fuzzy controller. The simulation experiments verified that the proposed method effectively addresses the navigation problem.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.94-96
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• 2021

In this paper, we develop a new navigation algorithm for industrial mobile robots to arrive at the destination in unknown environment. To achieve this, we suggest a navigation algorithm that combines Dynamic Window Approach (DWA) and Dijkstra path planning algorithm. We compare Local Dynamic Window Approach (LDWA), Global Dynamic Window Approach(GDWA), Rapidly-exploring Random Tree (RRT) Algorithm. The navigation algorithm using Dijkstra algorithm combined with LDWA and GDWA makes mobile robots to reach the destination. and obstacles faced during the path planning process of LDWA and GDWA. Then, we compare on time taken to arrive at the destination, obstacle avoidance and computation complexity of each algorithm. To overcome the limitation, we seek ways to use the optimized navigation algorithm for industrial use.

• Journal of Advanced Navigation Technology
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• v.19 no.6
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• pp.643-647
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• 2015

Nowadays, ubiquitous technology which combines sensors and network technology is emerging and it is called ubiquitous sensor network (USN). In this paper, mobile application for baby care using smart sensor is proposed. The proposed mobile application consists of mobile networks to transfer the information. It detects various information such as falling detecting, crying and fever detecting of infants. It keeps infants from external threats. The developed mobile application will be examined by simulation.

• Journal of the Institute of Electronics Engineers of Korea TE
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• v.42 no.2
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• pp.15-22
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• 2005

This paper represents the sensor combination technique of mobile robot to reduce the ambiguity and uncertainty of environment that prevents the mobile robot from recognizing the path planning and navigation. The sensors such as optical encoder, ultra sonar sensor, and infra-red sensor gathered the dynamic information of mobile robot that are used to detect the obstacle. Therefore, the mobile robot controller with sensor combination is stably demonstrated by the experimental results.

• Proceedings of the KIEE Conference
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• 2001.11c
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• pp.288-291
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• 2001

This paper present effective odometry error compensation using multisensor fusion for the accurate positioning of mobile robot in navigation. During obstacle avoidance and wall following of mobile robot, position estimates obtained by odometry become unrealistic and useless because of its accumulated errors. To measure the position and heading direction of mobile robot accurately, odometry sensor a gyroscope and an azimuth sensor are mounted on mobile robot and Complementary-filter is designed and implemented in order to compensate complementary drawback of each sensor and fuse their information. The experimental results show that the multisensor fusion system is more accurate than odometry only in estimation of the position and direction of mobile robot.