• Journal of the Korean Institute of Intelligent Systems
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• v.24 no.4
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• pp.437-442
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• 2014

Recently, the needs for the development and application of the micro marine robot (MMR) which has the advantages in terms of size and cost are increasing. However, the basis is very short in the domestic. While the obstacle avoidance sonar (OAS) which was optimized in terms of size and performance and has the ability of 4-directional detection was developed for the obstacle avoidance of the micro surface robot (MSR) fortunately, the problem that the detection performance is degraded according to the shape of the obstacle because of using the fixed sonar-beam with the limited beam width and detection range exists. To solve this problem, the MSR mechanism that implements the rotating sonar-beam using the spur gear and the servo motor is proposed in this paper. To verify the performance of the proposed mechanism, the wall-tracking of the MSR is considered and the comparison and analysis in term of detection performance and actuation command is performed with conventional fixed sonar-beam. The test results show the validity of the proposed mechanism.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.544-547
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• 1997

In this paper, we propose a dynamic localization method using a rotating sonar and a map. The proposed method is implemented by using extended Kalman filter. The state equation is based on the encoder propagation model and the encoder error model, and the measurement equation is a map-based measurement equation using a rotating sonar sensor. By utilizing sonar beam characteristics, map-based measurements are updated while AMR is moving continuously. By modeling and estimating systematic errors of a differential encoder, the position is successfully estimated even the interval of the map-based measurement. Monte-Carlo simulation shows that the proposed global position estimator has the performance of a few millimeter order in position error and of a few tenth degrees in heading error and of compensating systematic errors of the differential encoder well.