• Journal of Navigation and Port Research
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• v.27 no.3
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• pp.273-282
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• 2003

This paper proposes a sensor-fusion technique where rho data sets for the previous moments are properly transformed and fused into the current data sets to enable accurate measurement, such as, distance to an obstacle and location of the service robot itself. In the conventional fusion schemes, the measurement is dependent only on the current data sets. As the results, more of sensors are required to measure a certain physical promoter or to improve the accuracy of the measurement. However, in this approach, intend of adding more sensors to the system, the temporal sequence of the data sets are stored and utilized for the measurement improvement. Theoretical basis is illustrated by examples md the effectiveness is proved through the simulation. Finally, the new space and time sensor fusion (STSF) scheme is applied to the control of a mobile robot in the indoor environment and the performance was demonstrated by the real experiments.

• Journal of Institute of Control, Robotics and Systems
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• v.21 no.5
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• pp.453-458
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• 2015

We presented a quad-rotor controlling algorithm design by using sensor fusion in this paper. The controller design technique was performed by a PD controller with a Kalman filter and compensation algorithm for increasing the stability and reliability of the quad-rotor attitude. In this paper, we propose an attitude estimation algorithm for quad-rotor based sensor fusion by using the Kalman filter. For this reason, firstly, we studied the platform configuration and principle of the quad-rotor. Secondly, the bias errors of a gyro sensor, acceleration and geomagnetic sensor are compensated. The measured values of each sensor are then fused via a Kalman filter. Finally, the performance of the proposed algorithm is evaluated through experimental data of attitude estimation. As a result, the proposed sensor fusion algorithm showed superior attitude estimation performance, and also proved that robust attitude estimation is possible even in disturbance.

• Journal of Sensor Science and Technology
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• v.19 no.3
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• pp.246-257
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• 2010

Autonomous guided robot navigation which consists of following unknown paths and avoiding unknown obstacles has been a fundamental technique for unmanned robots in outdoor environments. The unknown path following requires techniques such as path recognition, path planning, and robot pose estimation. In this paper, we propose a novel sensor fusion system for autonomous guided robot navigation in outdoor environments. The proposed system consists of three monocular cameras and an array of nine infrared range sensors. The two cameras equipped on the robot's right and left sides are used to recognize unknown paths and estimate relative robot pose on these paths through bayesian sensor fusion method, and the other camera equipped at the front of the robot is used to recognize abrupt curves and unknown obstacles. The infrared range sensor array is used to improve the robustness of obstacle avoidance. The forward camera and the infrared range sensor array are fused through rule-based method for obstacle avoidance. Experiments in outdoor environments show the mobile robot with the proposed sensor fusion system performed successfully real-time autonomous guided navigation.

• Journal of Astronomy and Space Sciences
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• v.20 no.4
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• pp.359-364
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• 2003

In this paper, we present multi-sensor data fusion for telematics application. Successful telematics can be realized through the integration of navigation and spatial information. The well-determined acquisition of vehicle's position plays a vital role in application service. The development of GPS is used to provide the navigation data, but the performance is limited in areas where poor satellite visibility environment exists. Hence, multi-sensor fusion including IMU (Inertial Measurement Unit), GPS(Global Positioning System), and DMI (Distance Measurement Indicator) is required to provide the vehicle's position to service provider and driver behind the wheel. The multi-sensor fusion is implemented via algorithm based on Kalman filtering technique. Navigation accuracy can be enhanced using this filtering approach. For the verification of fusion approach, land vehicle test was performed and the results were discussed. Results showed that the horizontal position errors were suppressed around 1 meter level accuracy under simulated non-GPS availability environment. Under normal GPS environment, the horizontal position errors were under 40㎝ in curve trajectory and 27㎝ in linear trajectory, which are definitely depending on vehicular dynamics.

• Journal of the Korean Society for Railway
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• v.8 no.2
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• pp.155-160
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• 2005

We propose a tram position estimation method for automatic train control system. The accurate train position should be continuously feedback to control system for safe and efficient operation of trains in railway. In this paper, we propose the sensor fusion method integrating a tachometer, a transponder, and a doppler sensor far estimation of train position. The external sensors(transponder, doppler sensor) are used to compensate for the error of internal sensor (tachometer). The Kalman filter is also applied to reduce the measurement error of the sensors. Simulation results are then presented to verify the usefulness of the proposed method.

• Journal of Institute of Control, Robotics and Systems
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• v.15 no.4
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• pp.365-371
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• 2009

A intelligent mobile hexapod robot with various types of sensors and wireless camera is introduced. We show this mobile robot can detect objects well by combining the results of active sensors and image processing algorithm. First, to detect objects, active sensors such as infrared rays sensors and supersonic waves sensors are employed together and calculates the distance in real time between the object and the robot using sensor's output. The difference between the measured value and calculated value is less than 5%. This paper suggests effective visual detecting system for moving objects with specified color and motion information. The proposed method includes the object extraction and definition process which uses color transformation and AWUPC computation to decide the existence of moving object. We add weighing values to each results from sensors and the camera. Final results are combined to only one value which represents the probability of an object in the limited distance. Sensor fusion technique improves the detection rate at least 7% higher than the technique using individual sensor.

• Journal of the Korean Society of Industry Convergence
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• v.22 no.4
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• pp.427-433
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• 2019

This paper propose a fuzzy inference model for map building and navigation for a mobile robot with an active camera, which is intelligently navigating to the goal location in unknown environments using sensor fusion, based on situational command using an active camera sensor. Active cameras provide a mobile robot with the capability to estimate and track feature images over a hallway field of view. In this paper, instead of using "physical sensor fusion" method which generates the trajectory of a robot based upon the environment model and sensory data. Command fusion method is used to govern the robot navigation. The navigation strategy is based on the combination of fuzzy rules tuned for both goal-approach and obstacle-avoidance. To identify the environments, a command fusion technique is introduced, where the sensory data of active camera sensor for navigation experiments are fused into the identification process. Navigation performance improves on that achieved using fuzzy inference alone and shows significant advantages over command fusion techniques. Experimental evidences are provided, demonstrating that the proposed method can be reliably used over a wide range of relative positions between the active camera and the feature images.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.33B no.4
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• pp.41-51
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• 1996

This paper presents a fuzzy data fusion scheme which can analyze the sensor condition, the strength and location of a force applied to a test material. These can be realized by the modelling and fusioning of sensor signals and sensor properties. The technique uses, as the inference variables, relative magnitude of data (RMD), absolute magnitude of data (AMD) initial state (IS), synchronized relational function (SRF) and asynchronized relational function (ARF). To show the usefulness of this scheme, an experiment on the cantilever bar and six strain gages is carried out. The location of the force is inferred from SRF and ARF and the strength from RMD and AMD. In particular, the strength is compared with the measurement data of the force sensor.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.09a
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• pp.471-475
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• 2003

This paper proposes a sensor-fusion technique where the data sets for the previous moments are properly transformed and fused into the current data sets to enable accurate measurement, such as, distance to an obstacle and location of the service robot itself. In the conventional fusion schemes, the measurement is dependent on the current data sets. As the results, more of sensors are required to measure a certain physical parameter or to improve the accuracy of the measurement. However, in this approach, instead of adding more sensors to the system, the temporal sequence of the data sets are stored and utilized for the measurement improvement. Theoretical basis is illustrated by examples and the effectiveness is proved through the simulations. Finally, the new space and time sensor fusion (STSF) scheme is applied to the control of a mobile robot in an unstructured environment as well as structured environment.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.12 no.3
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• pp.245-249
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• 2012

In this paper, we propose a navigation algorithm for a mobile robot, which is intelligently searching the goal location in unknown dynamic environments using an ultrasonic sensor. Instead of using "sensor fusion" method which generates the trajectory of a robot based upon the environment model and sensory data, "command fusion" method is used to govern the robot motions. The navigation strategy is based on the combination of fuzzy rules tuned for both goal-approach and obstacle-avoidance. To identify the environments, a command fusion technique is introduced, where the sensory data of ultrasonic sensors and a vision sensor are fused into the identification process.