• 제어로봇시스템학회논문지
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• 제16권5호
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• pp.433-439
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• 2010

This paper proposes a beacon color code scheduling algorithm for the localization of multiple robots in a multi-block workspace. With the developments of intelligent robotics and ubiquitous technology, service robots are applicable for the wide area such as airports and train stations where multiple indoor GPS systems are required for the localization of the mobile robots. Indoor localization schemes using ultrasonic sensors have been widely studied due to its cheap price and high accuracy. However, ultrasonic sensors have some shortages of short transmission range and interferences with other ultrasonic signals. In order to use multiple robots in wide workspace concurrently, it is necessary to resolve the interference problem among the multiple robots in the localization process. This paper proposes an indoor localization system for concurrent multiple robots localization in a wide service area which is divided into multi-block for the reliable sensor operation. The beacon color code scheduling algorithm is developed to avoid the signal interferences and to achieve efficient localization with high accuracy and short sampling time. The performance of the proposed localization system is verified through the simulations and the real experiments.

• 제어로봇시스템학회논문지
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• 제16권3호
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• pp.293-299
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• 2010

The multiple block localization method in a wide area for multiple robots using iGS is proposed in this paper. The iGS is developed for the indoor global localization using ultrasonic and RF sensors. To measure the distance between a mobile robot and a beacon, the tag on the mobile robot wakes up one beacon to send out the ultrasonic signal and measures the traveling time from the beacon to the mobile robot. As the number of robots is increased, the sampling time of localization also becomes longer. Note that only one robot can localize its own position calling beacons one by one during each of the sampling interval. This is a severe constraint for the localization of multiple robots in a wide area. This paper proposes an efficient localization algorithm for the multiple robots in a wide area which can be divided into multiple blocks. For a given block, a master beacon is designated to synchronize robots. By the access of the synchronization signal, each beacon in the selected group sends out an ultrasonic signal. When the robots in the block receive the ultrasonic signal, they can calculate their own locations based on the distances to the beacons, which are obtained by the multiplication of flight time and velocity of the ultrasonic signal. The efficiency of the algorithm is verified through the real experiments.

• 제어로봇시스템학회논문지
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• 제22권2호
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• pp.104-109
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• 2016

In this paper, a localization method for multiple robots based on Bayesian inference is proposed when multiple robots adopting multi-RAT (Radio Access Technology) communications exist in cognitive radio networks. Multiple robots are separately defined by primary and secondary users as in conventional mobile communications system. In addition, the heterogeneous spectrum environment is considered in this paper. To improve the performance of localization for multiple robots, a realistic multiple primary user distribution is explained by using the probabilistic graphical model, and then we introduce the Gibbs sampler strategy based on Bayesian inference. In addition, the secondary user selection minimizing the value of GDOP (Geometric Dilution of Precision) is also proposed in order to overcome the limitations of localization accuracy with Gibbs sampling. Via the simulation results, we can show that the proposed localization method based on GDOP enhances the accuracy of localization for multiple robots. Furthermore, it can also be verified from the simulation results that localization performance is significantly improved with increasing number of observation samples when the GDOP is considered.

• 제어로봇시스템학회논문지
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• 제14권3호
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• pp.273-283
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• 2008

This article describes a cooperative localization technique of multiple robots sharing position information of each robot. In case of conventional methods such as EKF, they need to linearization process. Consequently, they are not able to guarantee that their result is range containing true value. In this paper, we propose a method to merge the data of redundant sensors based on constraints propagation techniques on intervals. The proposed method has a merit guaranteeing true value. Especially, we apply the constraints propagation technique fusing wheel encoders, a gyro, and an inexpensive GPS receiver. In addition, we utilize the correlation between GPS data in common workspace to improve localization performance for multiple robots. Simulation results show that proposed method improve considerably localization performance of multiple robots.

• 로봇학회논문지
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• 제2권3호
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• pp.255-261
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• 2007

We propose a optimal fusion method for localization of multiple robots utilizing correlation between GPS on each robot in common workspace. Each mobile robot in group collects position data from each odometer and GPS receiver and shares the position data with other robots. Then each robot utilizes position data of other robot for obtaining more precise estimation of own position. Because GPS data errors in common workspace have a close correlation, they contribute to improve localization accuracy of all robots in group. In this paper, we simulate proposed optimal fusion method of odometer and GPS through virtual robots and position data.

• 로봇학회논문지
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• 제2권1호
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• pp.93-102
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• 2007

It is essential to estimating positions of multiple robots in order to perform cooperative task in common workspace. Accordingly, we propose a new approach of cooperative localization for multiple robots utilizing correlation among GPS errors in common workspace. Assuming that GPS data of individual robot are correlated strongly as the distance among robots are close, it is confirmed that the proposed method provides improved localization accuracy. In addition, we define two operational parameters to apply proposed method in multiple robot system. With mentioned two parameters, we present a practical solution to accumulated position error in traveling long distance.

• ETRI Journal
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• 제37권2호
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• pp.262-272
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• 2015

In particular, for a practical mobile robot team to perform such a task as that of carrying out a search and rescue mission in a disaster area, the network connectivity and localization have to be guaranteed even in an environment where the network infrastructure is destroyed or a Global Positioning System is unavailable. This paper proposes the new collective intelligence network management architecture of multiple mobile robots supporting seamless network connectivity and cooperative localization. The proposed architecture includes a resource manager that makes the robots move around and not disconnect from the network link by considering the strength of the network signal and link quality. The location manager in the architecture supports localizing robots seamlessly by finding the relative locations of the robots as they move from a global outdoor environment to a local indoor position. The proposed schemes assuring network connectivity and localization were validated through numerical simulations and applied to a search and rescue robot team.

• 제어로봇시스템학회논문지
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• 제17권10호
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• pp.995-1005
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• 2011

An efficient outdoor local localization method is suggested using multi-sensor fusion with MU-EKF (Multi-Update Extended Kalman Filter) for car-like mobile robots. In outdoor environments, where mobile robots are used for explorations or military services, accurate localization with multiple sensors is indispensable. In this paper, multi-sensor fusion outdoor local localization algorithm is proposed, which fuses sensor data from LRF (Laser Range Finder), Encoder, and GPS. First, encoder data is used for the prediction stage of MU-EKF. Then the LRF data obtained by scanning the environment is used to extract objects, and estimates the robot position and orientation by mapping with map objects, as the first update stage of MU-EKF. This estimation is finally fused with GPS as the second update stage of MU-EKF. This MU-EKF algorithm can also fuse more than three sensor data efficiently even with different sensor data sampling periods, and ensures high accuracy in localization. The validity of the proposed algorithm is revealed via experiments.

• 로봇학회논문지
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• 제17권1호
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• pp.25-31
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• 2022

Moving among multiple floors is one of the most challenging tasks for indoor autonomous robots. Most of the previous researches for indoor mapping and localization have focused on singular floor environment. In this paper, we present an algorithm that creates a multi-floor map using 3D point cloud. We implement localization within the multi-floor map using a LiDAR and an IMU. Our algorithm builds a multi-floor map by constructing a single-floor map using a LOAM-based algorithm, and stacking them through global registration that aligns the common sections in the map of each floor. The localization in the multi-floor map was performed by adding the height information to the NDT (Normal Distribution Transform)-based registration method. The mean error of the multi-floor map showed 0.29 m and 0.43 m errors in the x, and y-axis, respectively. In addition, the mean error of yaw was 1.00°, and the error rate of height was 0.063. The real-world test for localization was performed on the third floor. It showed the mean square error of 0.116 m, and the average differential time of 0.01 sec. This study will be able to help indoor autonomous robots to operate on multiple floors.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2008년도 학술대회 논문집 정보 및 제어부문
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• pp.323-324
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• 2008

In this paper, we present performance comparison of two methods to localize multiple robots. One is extended Kalman filter and the other is constraint propagation technique. Extended Kalman filter is conventional probabilistic method which gives the sub-optimal estimation rather than guarantee any boundary for true position of robot. In case of constraint propagation, it can give a boundary containing true robot position value. Especially, we deal with cooperative localization problem in outdoor environment for multiple robots equipped with GPS, gyro meter, wheel encoder. In simulation results, we present strength and weakness for localization methods based on extend Kalman filter and constraint propagation technique.