兩輪機器人之深度影像障礙物偵測與人臉識別
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2018
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Abstract
本論文提出了將人臉識別功能與校正車體晃動測距誤差以及避障功能結合於兩輪機器人，使得機器人在照護及居家環境都能有更好的追蹤效果。兩輪機器人平衡或是移動時，會因為兩輪車機體晃動導致測距值的不穩定，本論文除了讓Kinect感應器的驅動馬達能自動使感應器保持水平外，也使用智慧型的預測方式來修正測距誤差值，增加測距值與避障決策的精準度。測距避障的部分則藉由Kinect感應器的深度影像完成，並將測距值與移動指令上傳至MySQL資料庫供兩輪機器人使用。在人臉識別的功能，本研究使用LBPH演算法完成，並以UART傳送資訊給兩輪機器人。並以實驗以及圖表驗證本論文提出的功能。
This thesis proposes to combine the correction of robotic body sway ranging error, obstacle avoidance function and face recognition function in two-wheeled robots for the purpose of enabling the robots to have better tracking results in both personal care and home environments. When the two-wheeled robots autonomously balances or moves, the ranging value will be unstable due to the shaking of the two-wheeled robots. In addition to using the Kinect sensor's drive motor to automatically maintain the level of sensor, this paper also uses a smart prediction method to correct the ranging error value in order to increase the accuracy of ranging value and the obstacle avoidance decision. The obstacle avoidance function is completed by the depth image of the Kinect sensor, and the ranging value and the movement instruction are uploaded to the MySQL database. This study uses the LBPH algorithm to complete the face recognition function and sends information to the two-wheeled robots with the universal asynchronous receiver-transmitter. Finally, in order to verify the proposed method, some experiments are performed.
This thesis proposes to combine the correction of robotic body sway ranging error, obstacle avoidance function and face recognition function in two-wheeled robots for the purpose of enabling the robots to have better tracking results in both personal care and home environments. When the two-wheeled robots autonomously balances or moves, the ranging value will be unstable due to the shaking of the two-wheeled robots. In addition to using the Kinect sensor's drive motor to automatically maintain the level of sensor, this paper also uses a smart prediction method to correct the ranging error value in order to increase the accuracy of ranging value and the obstacle avoidance decision. The obstacle avoidance function is completed by the depth image of the Kinect sensor, and the ranging value and the movement instruction are uploaded to the MySQL database. This study uses the LBPH algorithm to complete the face recognition function and sends information to the two-wheeled robots with the universal asynchronous receiver-transmitter. Finally, in order to verify the proposed method, some experiments are performed.
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兩輪機器人, 人臉識別, 測距與避障, 類神經網路, Kinect感應器, Two-wheeled robot, Face recognition, ranging and obstacle avoidance, neural network, Kinect sensor