• Proceedings of the Korean Society of Precision Engineering Conference
• /
• 2008.06a
• /
• pp.1-2
• /
• 2008

• Journal of the Korean Society for Precision Engineering
• /
• v.25 no.2
• /
• pp.35-42
• /
• 2008

This study introduces a robust feature detector for sonar data from a general fixed-type of sonar ring. The detector is composed of a data association filter and a feature extractor. The data association filter removes false returns provided frequently from sonar sensors, and classifies set of data from various objects and robot positions into a group in which all the data are from the same object. The feature extractor calculates the geometries of the feature for the group. We show the possibility of extracting circle feature as well as a line and a point features. The proposed method was applied to a real home environment with a real robot.

• The Journal of Korea Robotics Society
• /
• v.6 no.3
• /
• pp.247-254
• /
• 2011

This paper presents a method of sonar grid map matching for topological place recognition. The proposed method provides an effective rotation invariant grid map matching method. A template grid map is firstly extracted for reliable grid map matching by filtering noisy data in local grid map. Using the template grid map, the rotation invariant grid map matching is performed by Ring Projection Transformation. The rotation invariant grid map matching selects candidate locations which are regarded as representative point for each node. Then, the topological place recognition is achieved by calculating matching probability based on the candidate location. The matching probability is acquired by using both rotation invariant grid map matching and the matching of distance and angle vectors. The proposed method can provide a successful matching even under rotation changes between grid maps. Moreover, the matching probability gives a reliable result for topological place recognition. The performance of the proposed method is verified by experimental results in a real home environment.

• International Journal of Fuzzy Logic and Intelligent Systems
• /
• v.6 no.2
• /
• pp.161-166
• /
• 2006

Localization of mobile agent within a sensing network is a fundamental requirement for many applications, using networked navigating systems such as the sonar-sensing system or the visual-sensing system. To fully utilize the strengths of both the sonar and visual sensing systems, This paper describes a networked sensor-based navigation method in an indoor environment for an autonomous mobile robot which can navigate and avoid obstacle. In this method, the self-localization of the robot is done with a model-based vision system using networked sensors, and nonstop navigation is realized by a Kalman filter-based STSF(Space and Time Sensor Fusion) method. Stationary obstacles and moving obstacles are avoided with networked sensor data such as CCD camera and sonar ring. We will report on experiments in a hallway using the Pioneer-DX robot. In addition to that, the localization has inevitable uncertainties in the features and in the robot position estimation. Kalman filter scheme is used for the estimation of the mobile robot localization. And Extensive experiments with a robot and a sensor network confirm the validity of the approach.

• Journal of the Korean Society of Fisheries and Ocean Technology
• /
• v.36 no.2
• /
• pp.117-125
• /
• 2000

The waveform and spectrum analysis of Tursiops truncatus(bottlenose dolphin) sonar signals were carried out on the basis of data collected during the dolphin show at the aquarium of Cheju Pacificland from October 1998 to February 1999. When greeting to audience, the pulse width, peak frequency and spectrum level from the five dolphins'sonar signals were 3.0ms, 4.54kHz and 125.6dB, respectively. At the time of warm-up just before the show, their figures were 5.0㎳, 5.24kHz and 127.0dB, respectively. During the performance of dolphins, with singing, peak frequency ranged 3.28∼5.78kHz and spectrum level ranged 137.0∼142.0dB. With playing ring, pulse width, peak frequency and spectrum level were 7.0㎳, 2.54kHz and 135.9dB, and when playing the ball, the values were 9.0㎳, 2.78kHz and 135.2dB, respectively. The values determined from the five dolphins during jump-up out of water were : pulse width 2.0㎳, peak frequency 4.50kHz and spectrum level 126.8dB. When they responded to trainer's instructions, the values were 2.25㎳, 248kHz and 148.7dB, respectively, and greeting to audience, the peak frequency and spectrum level were 5.84kHz and 122.5dB. During swimming under water, peak frequency and spectrum level were determined to be 10.10kHz and 126.8dB. It was found that there exited close consistencies in pulse width, frequency distribution and spectrum level between whistle sounds and dolphin's sonar signals. Accordingly, the dolphins can be easily trained by using whistle sound based on the results obtained from the waveform and spectrum of the dolphin's sonar signals.