An Obstacle Avoidance Method for Indoor Flaw Detection Unmanned Robot Based on Transfer Neural Network
Publication: Earth and Space 2021
ABSTRACT
The unmanned vehicle can automatically evaluate the structural safety of the building by carrying a visual crack detection camera. However, the complexity of layout within the building requires that unmanned flaw detection robot has high obstacle avoidance performance. Aiming at the difficulty of obstacle avoidance in complex environment, the direction control method based on image sensor is adopted. The image is transmitted to the convolution neural network (CNN), and the direction instruction of the neural network is output to the motor driver to realize the autonomous obstacle avoidance of the wheeled robot. Data sets are automatically classified by arranging camera arrays. In this paper, the transfer neural network is used to reconstruct the neural network by discarding the last three layers and setting up new three layers. The accuracy of the neural network trained by the simulation results can reach 89.01%.
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Earth and Space 2021
Pages: 484 - 493
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© 2021 American Society of Civil Engineers.
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Published online: Apr 15, 2021
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