• Journal of the Korean Institute of Intelligent Systems
• /
• v.18 no.4
• /
• pp.537-542
• /
• 2008

Cricket is an excellent indoor location system and it can successfully solve many critical problems such as user privacy, decentralized administration. But in some practical applications, Cricket sometimes didn't provide location with enough accuracy, and was unable to determine when it was giving inaccurate information. For getting high-accuracy tracking performance from location data contaminated with noise, some types of filters are required. Kalman Filter is an efficient recursive filter that estimates the state of a dynamic system from a series of incomplete and noisy measurement. The filter is very powerful in the field of autonomous and assisted navigation. In this paper, we carry out comparative studies to validate the performance of the application of Kalman Filter to Cricket based localization system.

• IEMEK Journal of Embedded Systems and Applications
• /
• v.14 no.3
• /
• pp.151-156
• /
• 2019

According to recent advances in technology, major robot technologies that have been developed and commercialized for industrial use are being applied to various fields in our everyday life such as guide robots and cleaning robots. Among them, the navigation based on the self localization has become an essential element technology of the robot. In the case of indoor environment, many high-priced sensors are used, which makes it difficult to activate the robot industry. In this paper, we propose a robotic platform and a moving algorithm that can travel by using Dijkstra algorithm. The proposed system can find a short route to the destination with its own position. Also, its performance is discussed through the experimentation of an actual robot.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2023.11a
• /
• pp.624-627
• /
• 2023

Unmanned aerial vehicles are gaining in popularity with the development of science and technology, and are being used for a wide range of purposes, including surveillance, rescue, delivery of goods, and data collection. In particular, the ability to avoid obstacles during navigation without human oversight is one of the essential capabilities that a drone must possess. Many works currently have solved this problem by implementing deep reinforcement learning (DRL) model. The essential core of a DRL model is reward function. Therefore, this paper proposes a new reward function with appropriate action space and employs dueling double deep Q-Networks to train a drone to navigate in indoor environment without collision.

• Journal of Institute of Control, Robotics and Systems
• /
• v.14 no.2
• /
• pp.141-147
• /
• 2008

In this paper, we present one approach to achieve safe navigation in indoor dynamic environment. So far, there have been various useful collision avoidance algorithms and path planning schemes. However, those algorithms have a fundamental limitation that the robot can avoid only "visible" obstacles. In real environment, it is not possible to detect all the dynamic obstacles around the robot. There exist a lot of "occluded" regions due to the limitation of field of view. In order to avoid possible collisions, it is desirable to consider visibility information. Then, a robot can reduce the speed or modify a path. This paper proposes a safe navigation scheme to reduce the risk of collision due to unexpected dynamic obstacles. The robot's motion is controlled according to a hybrid control scheme. The possibility of collision is dually reflected to a path planning and a speed control. The proposed scheme clearly indicates the structural procedure on how to model and to exploit the risk of navigation. The proposed scheme is experimentally tested in a real office building. The presented result shows that the robot moves along the safe path to obtain sufficient field of view, while appropriate speed control is carried out.

• The Journal of the Korea institute of electronic communication sciences
• /
• v.8 no.3
• /
• pp.421-432
• /
• 2013

This paper describes implementation of functions for mobile robot localization, which is one of the vital technologies for autonomous navigation of a mobile robot. There are several function libraries for mobile robot navigation. Some of them have limited applicability for practical use since they can be used only for simulation. Our research focuses on development of functions which can be used for localization of indoor robots. The functions implement deadreckoning and motion model of mobile robots, measurement model of range sensors, and frequently used calculations on angular directions. The functions encompass various types of robots and sensors. Also, various types of uncertainties in robot motion and sensor measurements are implemented so that the user can select proper ones for their use. The functions are tested and verified through simulation and experiments.

• Journal of Positioning, Navigation, and Timing
• /
• v.2 no.1
• /
• pp.91-100
• /
• 2013

This paper introduces an indoor sensor fusion wireless communication device which provides the Location Based Service (LBS) using fire prevention facility. The proposed system can provide information in real time by optimizing the hardware of Wi-Fi technology. The proposed system can be applied to a fire prevention facility (i.e., emergency exit) and provide information such as escape way, emergency exit location, and accident alarm to smart phone users, dedicated terminal holders, or other related organizations including guardians, which makes them respond instantly with lifesaving, emergency mobilization, etc. Also, the proposed system can be used as a composite fire detection sensor node with additional fire and motion detect sensors.

• Journal of Sensor Science and Technology
• /
• v.33 no.4
• /
• pp.196-202
• /
• 2024

Path-planning, a critical component of mobile robot navigation, comprises both local and global planning. Previous studies primarily focused on enhancing the individual performance of these planners, avoiding obstacles, and computing an optimal global path from a starting position to a target position. In this study, we introduce a practical path-planning framework that employs a target planner to bridge the local and global planners; this enables mobile robots to navigate seamlessly and efficiently toward a global target position. The proposed target planner assesses the visibility of waypoints along the global path, and it selects a reachable navigation target, which can then be used to generate efficient control commands for the local planners. A visibility-based target planner can handle situations, wherein the current, target waypoint is occupied by unknown obstacles. Real-world experiments demonstrated that the proposed pathplanning framework with the visibility-based target planner allowed the robot to navigate to the final target position along a more efficient path than the framework without a target planner.

• The Journal of Korea Robotics Society
• /
• v.18 no.4
• /
• pp.487-495
• /
• 2023

This paper presents a method for autonomous exploration of indoor environments using a 2-dimensional Light Detection And Ranging (LiDAR) scanner. The proposed frontier-based exploration method considers navigability from the current robot position to extracted frontier targets. An approach to constructing the point cloud grid map that accurately reflects the occupancy probability of glass obstacles is proposed, enabling identification of safe frontier grids on the safety grid map calculated from the point cloud grid map. Navigability, indicating whether the robot can successfully navigate to each frontier target, is calculated by applying the skeletonization-informed rapidly exploring random tree algorithm to the safety grid map. While conventional exploration approaches have focused on frontier detection and target position/direction decision, the proposed method discusses a safe navigation approach for the overall exploration process until the completion of mapping. Real-world experiments have been conducted to verify that the proposed method leads the robot to avoid glass obstacles and safely navigate the entire environment, constructing the point cloud map and calculating the navigability with low computing time deviation.

• Tunnel and Underground Space
• /
• v.32 no.3
• /
• pp.231-242
• /
• 2022

In this study, we developed a robot operating system (ROS)-based autonomous driving robot that estimates the robot's position in underground mines and drives and returns through multiple waypoints. Autonomous driving robots utilize SLAM (Simultaneous Localization And Mapping) technology to generate global maps of driving routes in advance. Thereafter, the shape of the wall measured through the LiDAR sensor and the global map are matched, and the data are fused through the AMCL (Adaptive Monte Carlo Localization) technique to correct the robot's position. In addition, it recognizes and avoids obstacles ahead through the LiDAR sensor. Using the developed autonomous driving robot, experiments were conducted on indoor experimental sites that simulated the underground mine site. As a result, it was confirmed that the autonomous driving robot sequentially drives through the multiple waypoints, avoids obstacles, and returns stably.

• Journal of Advanced Navigation Technology
• /
• v.25 no.6
• /
• pp.441-449
• /
• 2021

With advances in autonomous vehicles, there is a growing demand for more accurate position estimation. Especially, this is a case for a moving robot for the indoor operation which necessitates the higher accuracy in position estimation when the robot is required to execute the task at a predestined location. Thus, a method for improving the position estimation which is applicable to both the fixed and the moving object is proposed. The proposed method exploits the initial position estimation from Bluetooth beacon signals as observation signals. Then, it estimates the gravitational acceleration applied to each axis in an inertial frame coordinate through computing roll and pitch angles and combining them with magnetometer measurements to compute yaw angle. Finally, it refines the control inputs for an object with motion dynamics by computing acceleration on each axis, which is used for improving the performance of Kalman filter. The experimental assessment of the proposed algorithm shows that it improves the position estimation accuracy in comparison to a conventional Kalman filter in terms of average error distance at both the fixed and moving states.