• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.1448-1451
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• 2004

Position estimation is one of the most important functions for the mobile robot navigating in the unstructured environment. Most of previous localization schemes estimate current position and pose of mobile robot by applying various localization algorithms with the information obtained from sensors which are set on the mobile robot, or by recognizing an artificial landmark attached on the wall, or objects of the environment as natural landmark in the indoor environment. Several drawbacks about them have been brought up. To compensate the drawbacks, a new localization method that estimates the absolute position of the mobile robot by using a fixed camera on the ceiling in the corridor is proposed. And also, it can improve the success rate for position estimation using the proposed method, which calculates the real size of an object. This scheme is not a relative localization, which decreases the position error through algorithms with noisy sensor data, but a kind of absolute localization. The effectiveness of the proposed localization scheme is demonstrated through the experiments.

• Journal of Sensor Science and Technology
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• v.26 no.3
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• pp.199-203
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• 2017

In this paper, we developed an underwater localization system for underwater robot docking using the electromagnetic wave attenuation model. Electromagnetic waves are generally known to be impossible to use in water environment. However, according to the conclusions of the previous studies on the attenuation characteristics in underwater, the attenuation pattern is uniform and its model was accurately proposed and verified in 3-dimensional space via the omnidirectional antenna. In this paper, a docking structure and localization sensor system are developed for a widely used cone type docking mechanism. First, we fabricated electromagnetic wave range sensor transmit modules. And a mobile sensor node is equipped with unmanned underwater vehicle(UUV)s. The mobile node senses the four different signal strength (RSS: Received Signal Strength) from fixed nodes, and the obtained RSS data are transformed to each distance information using the 3-Dimensional EM wave attenuation model. Then, the relative localization between the docking area and underwater robot can be achieved according to optimization algorithm. Finally, experimental results show the feasibility of the proposed localization system for the docking induction by comparing the errors in the actual position of the mobile node and the theoretical position through the model.

• Journal of Institute of Control, Robotics and Systems
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• v.22 no.7
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• pp.562-569
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• 2016

This paper proposes a navigation system with a robust localization method for an underwater unmanned vehicle. For robust localization with IMU (Inertial Measurement Unit), a DVL (Doppler Velocity Log), and depth sensors, the EKF (Extended Kalman Filter) has been utilized to fuse multiple nonlinear data. Note that the GPS (Global Positioning System), which can obtain the absolute coordinates of the vehicle, cannot be used in the water. Additionally, the DVL has been used for measuring the relative velocity of the underwater vehicle. The DVL sensor measures the velocity of an object by using Doppler effects, which cause sound frequency changes from the relative velocity between a sound source and an observer. When the vehicle is moving, the motion trajectory to a target position can be recorded by the sensors attached to the vehicle. The performance of the proposed navigation system has been verified through real experiments in which an underwater unmanned vehicle reached a target position by using an IMU as a primary sensor and a DVL as the secondary sensor.

• Journal of Institute of Control, Robotics and Systems
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• v.18 no.3
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• pp.161-167
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• 2012

It is known that big brown bats can distinguish echo of a prey at various angles. In this paper, we suggest a new object localization strategy using ultrasonic echolocation. We calculate the relative energy ratio between a high frequency component of ultrasound signal and a low frequency component of ultrasound signal for a target object. We found the measure depends on bearing angle of the object in space. We also tested energy ratio of echoed FM ultrasound signals depending on frequency, based on cross-correlation. It can determine the relative angular position of objects even though the reflected signals are congested form each object.

• The KIPS Transactions:PartC
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• v.17C no.3
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• pp.251-258
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• 2010

In an indoor localization method taking the lateration-based approach, the location of a target is estimated with the location of anchor points (APs) and the approximated distances between the target and APs using received signal strength (RSS) measurements. The accuracy of distance estimation affects the localization accuracy of a lateration-based method. Since a radio propagation environment varies randomly in time and space, the highest RSSs do not necessarily give the best estimation of the distances between a target and APs. Thus, all APs hearing a target have been used for localization. However, the accuracy of a lateration-based method degrades if more APs beyond a certain threshold are used because the area of polygon with the APs increases. In this paper, we focus on reducing the size of the polygon to further increase the localization accuracy. We use the centroid of the polygon as a reference point to estimate the relative location of a target in the polygon. Once the relative location is estimated, only the APs which are closest to the target are used for localization to reduce the area of the polygon with the APs. We validate the proposed method by implementing an indoor localization system and evaluating the accuracy of the proposed method in the various experimental environments.

• Journal of Institute of Control, Robotics and Systems
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• v.14 no.9
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• pp.931-938
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• 2008

In this paper, a novel localization algorithm robust to the unmodeled systematic odometry errors is proposed for low-cost non-holonomic mobile robots. It is well known that the most pose estimators using odometry measurements cannot avoid the performance degradation due to the dead-reckoning of systematic odometry errors. As a remedy for this problem, we tty to reflect the wheelbase error in the robot motion model as a parametric uncertainty. Applying the Krein space estimation theory for the discrete-time uncertain nonlinear motion model results in the extended robust Kalman filter. This idea comes from the fact that systematic odometry errors might be regarded as the parametric uncertainties satisfying the sum quadratic constrains (SQCs). The advantage of the proposed methodology is that it has the same recursive structure as the conventional extended Kalman filter, which makes our scheme suitable for real-time applications. Moreover, it guarantees the satisfactoty localization performance even in the presence of wheelbase uncertainty which is hard to model or estimate but often arises from real driving environments. The computer simulations will be given to demonstrate the robustness of the suggested localization algorithm.

• Journal of Advanced Marine Engineering and Technology
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• v.38 no.2
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• pp.188-194
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• 2014

Recently, indoor localization systems based on wireless sensor networks have received a great deal of attention because they help achieve high accuracy in position determination by using various algorithms. In order to minimize the error in the estimated azimuth that can occur owing to sensor drift and recursive calculation in these algorithms, we propose a novel relative azimuth estimation algorithm. The advantages of the proposed technique in an indoor environment are that an improved weight average filter is used to effectively reduce impulse noise from the raw data acquired from nodes with inherent errors and a rotational displacement algorithm is applied to obtain a precise relative azimuth without using additional sensors, which can be affected by electromagnetic noise. Results from simulations show that the proposed filter reduces the impulse noise, and the acquired estimation error does not accumulate with time by using proposed algorithm.

• Journal of information and communication convergence engineering
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• v.10 no.4
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• pp.390-395
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• 2012

This paper describes an implementation of automatic mutual localization of swarm robots using a particle filter. Each robot determines the location of the other robots using wireless sensors. The measured data will be used for determination of the movement method of the robot itself. It also affects the other robots' self-arrangement into formations such as circles and lines. We discuss the problem of a circle formation enclosing a target that moves. This method is the solution for enclosing an invader in a circle formation based on mutual localization of the multi-robot without infrastructure. We use trilateration, which does require knowing the value of the coordinates of the reference points. Therefore, specifying the enclosure point based on the number of robots and their relative positions in the coordinate system. A particle filter is used to improve the accuracy of the robot's location. The particle filter is operates better for mutual location of robots than any other estimation algorithms. Through the experiments, we show that the proposed scheme is stable and works well in real environments.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2018.05a
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• pp.241-243
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• 2018

In this paper, in Simultaneous localization and mapping (SLAM), the robot acquire its map of environment while simultaneously localizing itself relative to the map. Cloud based SLAM, allows us to optimizing resource and data sharing like map of the environment, which allows us, as one of shared available online map. Doing so, unless we add or remove significant change in our environment, the essence of rebuilding new environmental map are omitted to new mobile robot added to the environment. As result, the requirement of additional sensor are curtailed.

• Proceedings of the KIEE Conference
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• 2001.11c
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• pp.91-94
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• 2001

In this paper, we proposed localization method of mobile robot using color landmark mounted on ceiling. This work is composed 2 parts : landmark recognition part which finds the position of multiple landmarks in image and identifies them and absolute position estimation part which estimates the location and orientation of mobile robot in indoor environment. In landmark recognition part, mobile robot detects artificial color landmarks using simple histogram intersection method in rg color space which is insensitive to the change of illumination. Then absolute position estimation part calculates relative position of the mobile robot to the detected landmarks. For the verification of proposed algorithm, ceiling-orientated camera was installed on a mobile robot and performance of localization was examined by designed artificial color landmarks. As the result of test, mobile robot could achieve the reliable landmark detection and accurately estimate the position of mobile robot in indoor environment.