A novel configuration of ultrasonic sensors for mobile robots

This study suggests a method to measure the distance to an object using a stereo vision system(SVS) by emulating human vision system. Distance measurement methods of previous studies have the downside that the distance can be measured only if an object is located between the optical axes in the overlapping area of stereo cameras. Thus, this study suggests a method to measure the distance when an object is located outside the optical axes in the overlapping area of the two cameras. Simulations were conducted to test the distance measurement method suggested in this study. The simulations proved the accuracy of the suggested distance measurement method.

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“…P r and P l of Fig. 4 amount to negative number and D 1 , D 2 is calculated in equation (3) and equation (4). …”

Section: B Distance Measurement By Parallel Camera Setup Methodsmentioning

confidence: 99%

Improvement of Distance Measurement Algorithm on Stereo Vision System(SVS)

Baek

Choi

Lee

2010

2010 Proceedings of the 5th International Conference on Ubiquitous Information Technologies and Applications

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Image-based visual servoing for mobile robots using neural networks and fuzzy-evolutionary methods

Proceedings of the 2002 International Joint Conference on Neural Networks. IJCNN'02 (Cat. No.02CH37290)

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This paper presents a new image based visual servoing method, which plans the navigation trajectory in image plane for non-holonomic mobile robots. It does not require camera calibration nor coordinate transformation from image space to workspace. The robot navigates along a preplanned workspace trajectory while at the same time it learns, using neural networks, a desired or reference landmark trajectory in the image space. Each point of the reference trajectory is basically what is seen of the target landmark at each control cycle. After this network training, the robot compares the current landmark with not only the target landmark but also the reference landmark along the way. Using a fuzzy logic to control this error the mobile robot navigates through the learned trajectory in the workspace. The fuzzy logic is optimized by an evolutionary algorithm to meet a userdefined objective. The proposed algorithm has been verified via simulation.

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