Interactions Between Specific Human and Omnidirectional Mobile Robot Using Deep Learning Approach: SSD-FN-KCF

• Main Authors: Ding-Sheng Wang, 王鼎升
• Other Authors: Chih-Lyang Hwang
• Format: Others
• Language: zh-TW
• Published: 2019
• Online Access: http://ndltd.ncl.edu.tw/handle/q6pk6c

碩士 === 國立臺灣科技大學 === 電機工程系 === 107 === To fulfill the tasks of human-robot interactions, how to detect the specific human (SH) becomes paramount. In this paper, the deep learning approach：SSD-FN-KCF by the integration of Single-Shot Detection(SSD), FaceNet(FN), and Kernelized Correlation Filter (KCF) is developed. From the outset, the SSD is employed to detect the human up to 8m using RGB-D camera with the resolution of After that, the omnidirectional mobile robot (ODMR) is commanded to the neighborhood of 3.0m such that the depth image can accurately estimate the detected human’s pose. Then the ODMR is commanded to the vicinity of 1.0m and 0 with respect to the optical axis to identify whether he/she is the SH by FaceNet. To reduce the computation time of FaceNet and extend the tracking of the SH, the KCF accomplished the goal for the human-robot interactions (e.g., human following). Based on the information of image processing, the required pose for searching or tracking (specific) human is also accomplished by the ODMR with the image-based adaptive finite-time hierarchical constraint control (IB-AFTHCC). Finally, compared experiments between SH and ODMR validate the effectiveness and robustness of the proposed control.