面向防疫的智能导诊机器人关键技术及应用

doi:10.11959/j.issn.2096-6652.202119

智能科学与技术学报 ›› 2021, Vol. 3 ›› Issue (2): 187-194.doi: 10.11959/j.issn.2096-6652.202119

面向防疫的智能导诊机器人关键技术及应用

王腾¹, 潘晶², 董璐³, 孙长银¹

¹ 东南大学自动化学院，江苏南京210018
² 上海钛米机器人股份有限公司，上海201203
³ 同济大学电子与信息工程学院，上海201804

修回日期:2020-11-16 出版日期:2021-06-15 发布日期:2021-06-01
作者简介:王腾（1988- ），女，博士，东南大学自动化学院讲师，主要研究方向为机器人环境感知与视觉导航
潘晶（1980- ），男，上海钛米机器人股份有限公司董事长兼 CEO，主要研究方向为智能医疗服务机器人设计与制造
董璐（1990- ），女，博士，同济大学电子与信息工程学院副研究员，主要研究方向为机器人智能控制
孙长银（1975- ），男，博士，东南大学自动化学院教授、博士生导师，主要研究方向为机器人环境感知与智能控制
基金资助:
国家自然科学基金资助项目(61803084);国家自然科学基金资助项目(62041301)

Key technologies and applications of intelligent guiding robots for epidemic prevention

Teng WANG¹, Jing PAN², Lu DONG³, Changyin SUN¹

¹ School of Automation, Southeast University, Nanjing 210018, China
² Taimi Robotics Technology Co., Ltd., Shanghai 201203, China
³ School of Electronical and Information Engineering, Tongji University, Shanghai 201804, China

Revised:2020-11-16 Online:2021-06-15 Published:2021-06-01
Supported by:
The National Natural Science Foundation of China(61803084);The National Natural Science Foundation of China(62041301)

摘要/Abstract

摘要：

疫情防控期间，智能机器人在医疗、巡检和配送等领域走上防疫一线，成为抗疫战斗中一股极为重要的力量，使得更多的人意识到智能机器人在解决公共卫生安全问题中的重要性。重点关注面向防疫的智能导诊机器人，首先探讨智能导诊机器人的关键技术及其研究现状，包括机器人消毒、人脸识别、语音交互、自主定位、路径规划等；然后以钛米智能导诊机器人为应用案例，阐述上述关键技术在实际应用中的落脚点；最后对智能导诊机器人的发展趋势做出展望。

关键词: 机器人消毒, 人脸识别, 语音交互, 自主定位, 路径规划

Abstract:

During the period of epidemic prevention, intelligent robots play a significant role in the fields of medical treatment, inspection and distribution, making more people realize the importance of robots in solving public health and safety problems.Focusing on the intelligent guiding robots, the key technologies and research status of the intelligent guiding robots were discussed, including robot disinfection, face recognition, speech interaction, autonomous localization, path planning.Then, taking TMiRob intelligent guiding robot as an example to discuss how these key technologies were implemented in reality.Finally, the challenges and development trends of intelligent guiding robots were presented.

Key words: robot disinfection, face recognition, speech interaction, autonomous localization, path planning

中图分类号:

TP242.6

王腾, 潘晶, 董璐, 等. 面向防疫的智能导诊机器人关键技术及应用[J]. 智能科学与技术学报, 2021, 3(2): 187-194.

Teng WANG, Jing PAN, Lu DONG, et al. Key technologies and applications of intelligent guiding robots for epidemic prevention[J]. Chinese Journal of Intelligent Science and Technology, 2021, 3(2): 187-194.

图/表 4

参考文献 46

[1]	黄永安, 熊蔡华, 熊有伦 . 智能机器人与应用的现状与发展趋势[J]. 国际学术动态, 2009: 38-39.
	HUANG Y A , XIONG C H , XIONG Y L . Status and development trend of intelligent robots and applications[J]. International Academic Developments, 2009: 38-39.
[2]	徐扬生 . 智能机器人引领高新技术发展[J]. 企业科协, 2010(9): 28-31.
	XU Y S . Intelligent robots lead high-tech development[J]. Enterprise Association, 2010(9): 28-31.
[3]	谭民, 王硕 . 机器人技术研究进展[J]. 自动化学报, 2013,39(7): 963-972.
	TAN M , WANG S . Research progress on robotics[J]. Acta Automatica Sinica, 2013,39(7): 963-972.
[4]	HE J X , BAXTER S L , XU J ,et al. The practical implementation of artificial intelligence technologies in medicine[J]. Nature Medicine, 2019: 30-36.
[5]	TAIGMAN Y , YANG M , RANZATO M ,et al. DeepFace:closing the gap to human-level performance in face verification[C]// Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Piscataway:IEEE Press, 2014.
[6]	SUN Y , WANG X , TANG X . Deep learning face representation from predicting 10000 classes[C]// Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Piscataway:IEEE Press, 2014.
[7]	PARKHI O M , VEDALDI A , ZISSERMAN A ,et al. Deep face recognition[C]// Proceedings of the British Machine Vision Conference.[S.l.:s.n.], 2015.
[8]	SCHROFF F , KALENICHENKO D , PHILBIN J . FaceNet:a unified embedding for face recognition and clustering[C]// Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Piscataway:IEEE Press, 2015.
[9]	DENG J , GUO J , ZAFEIRIOUS S . ArcFace:additive angular margin loss for deep face recognition[J]. arXiv preprint,2018,arXiv:1801.07698.
[10]	CHEN D , CAO X , WANG L ,et al. Bayesian face revisited:a joint formulation[C]// Proceedings of the 12th European Conference on Computer Vision-Volume Part III. Berlin:Springer, 2012: 566-579.
[11]	SANKARANARAYANAN S , ALAVI A , CHELLAPPA R . Triplet similarity embedding for face verification[J]. arXiv preprint,2016,arXiv:1602.03418.
[12]	SANKARANARAYANAN S , ALAVI A , CASTILLO C D ,et al. Triplet probabilistic embedding for face verification and clustering[C]// Proceedings of the 2016 IEEE 8th International Conference on Biometrics Theory,Applications and Systems. Piscataway:IEEE Press, 2016.
[13]	KUMARB G V , CARNEIRO G , REID I . Learning local image descriptors with deep Siamese and triplet convolutional networks by minimizing global loss functions[C]// Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Piscataway:IEEE Press, 2016.
[14]	TRIGUEROS D S , MENG L , HARTNETT M . Enhancing convolutional neural networks for face recognition with occlusion maps and batch triplet loss[J]. Image and Vision Computing, 2017(79).
[15]	SABOUR S , FROSST N , HINTON G E . Dynamic routing between capsules[C]// Proceedings of the 31st Conference and Workshop on Neural Information Processing Systems.[S.l.:s.n.], 2017.
[16]	ZHANG C , LI H , CHEN C ,et al. Enhanced group sparse regularized nonconvex regression for face recognition[C]// Proceedings of the IEEE Transactions on Pattern Analysis and Machine Intelligence. Piscataway:IEEE Press, 2020.
[17]	ELMAHMUDI A , UGAIL H . Deep face recognition using imperfect facial data[J]. Future Generation Computer Systems, 2019,99: 213-225.
[18]	DAHLG E , YU D , DENG L ,et al. Context-dependent pre-trained deep neural networks for large-vocabulary speech recognition[J]. IEEE Transactions on Audio,Speech,and Language Processing, 2011,20(1): 30-42.
[19]	VINYALS O , RAVURI S V , POVEY D . Revisiting recurrent neural network for robust ASR[C]// Proceedings of the 37th International Conference on Acoustics,Speech and Signal Processing. Piscataway:IEEE Press, 2012.
[20]	MAAS A L , LE Q V , O’NEIL T M ,et al. Recurrent neural networks for noise reduction in robust ASR[C]// Proceedings of the INTERSPEECH. Piscataway:IEEE Press, 2012.
[21]	GRAVESA , JAITLY N , MOHAMED A R . Hybrid speech recognition with deep bidirectional LSTM[C]// Proceedings of the 2013 IEEE Workshop on Automatic Speech Recognition and Understanding. Piscataway:IEEE Press, 2013.
[22]	ABDEL-HAMID O , MOHAMED A , JIANG H ,et al. Convolutional neural networks for speech recognition[J]. IEEE Transactions on Audio,Speech,and Language Processing, 2014,22(10): 1533-1545.
[23]	SAINATH T N , VINYALS O , SAK H . Convolutional,long short-term memory,fully connected deep neural networks[C]// Proceedings of the 40th IEEE International Conference on Acoustics,Speech and Signal Processing. Piscataway:IEEE Press, 2015.
[24]	GRAVES A , FERNANDEZ S , GOMEZ F ,et al. Connectionist temporal classification:labelling unsegmented sequence data with recurrent neural networks[C]// Proceedings of the 23rd International Conference on Machine Learning.[S.l.:s.n.], 2006: 25-29.
[25]	HANNUN A , CASE C , CASPER J ,et al. Deep Speech:scaling up end-to-end speech recognition[J]. arXiv preprint,2014,arXiv:1412.5567.
[26]	AMODEI D , ANANTHANARAYANAN S , ANUBHAI R ,et al. Deep Speech 2:end-to-end speech recognition in English and Mandarin[C]// Proceedings of the 33rd International Conference on Machine Learning.[S.l.:s.n.], 2016.
[27]	SOLTAU H , LIAO H , SAK H . Neural speech recognizer:acoustic-to-word LSTM model for large vocabulary speech recognition[C]// Proceedings of the INTERSPEECH.[S.l.:s.n.], 2017.
[28]	GRAVES A , . Sequence transduction with recurrent neural networks[C]// International Conference on Machine Learning.[S.l.:s.n.], 2012.
[29]	CHAN W , JAITLY N , LE Q ,et al. Listen,attend and spell:a neural network for large vocabulary conversation[C]// Proceedings of the 41st International Conference on Acoustics,Speech and Signal Processing. Piscataway:IEEE Press, 2016.
[30]	CHIUC C , SAINATH T N , WU Y ,et al. State-of-the-art speech recognition with sequence-to-sequence models[C]// Proceedings of the 43rd International Conference on Acoustics,Speech and Signal Processing. Piscataway:IEEE Press, 2018.
[31]	OORD A V D , DIELEMAN S , ZEN H ,et al. WaveNet:a generative model for raw audio[J]. arXiv preprint,2016,arXiv:1609.03499.
[32]	ARIKS O , CHRZANOWSKI M , COATES A ,et al. Deep Voice:real-time neural text-to-speech[C]// Proceedings of the 31st International Conference on Machine Learning.[S.l.:s.n.], 2017.
[33]	WANG Y , SKERRY-RYAN R J ,, STANTON D , et al . Tacotron:towards end-to-end speech synthesis[C]// Proceedings of the 31st INTERSPEECH.[S.l.:s.n.], 2017.
[34]	ARIK S , DIAMOS G , GIBINASKY A ,et al. Deep Voice 2:multi-speaker neural text-to-speech[C]// Proceedings of the 31st Conference and Workshop on Neural Information Processing Systems.[S.l.:s.n.], 2017.
[35]	PING W , PENG K , GIBINASKY A ,et al. Deep Voice 3:scaling text-to-speech with convolutional sequence learning[C]// Proceedings of the 6th International Conference on Learning Representations.[S.l.:s.n.], 2018.
[36]	OORD A V D , LI Y , BABUSCHKIN I ,et al. Parallel WaveNet:fast high-fidelity speech synthesis[C]// Proceedings of Machine Learning Research.[S.l.:s.n.], 2018.
[37]	SHEN J , PANG R , WEISSR J ,et al. Natural TTS synthesis by conditioning WaveNet on mel spectrogram predictions[C]// Proceedings of the 43rd International Conference on Acoustics,Speech and Signal Processing. Piscataway:IEEE Press, 2018.
[38]	YE H , CHEN Y , LIU M . Tightly coupled 3D Lidar inertial odometry and mapping[C]// Proceedings of the International Conference on Robotics and Automation.[S.l.:s.n.], 2019.
[39]	GRAETER J , WILCZYNSKI A , LAUER M . LIMO:Lidar-Monocular visual odometry[C]// Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems. Piscataway:IEEE Press, 2018.
[40]	ZHANG J , KAESS M , SINGH S . Real-time depth enhanced monocular odometry[C]// Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems. Piscataway:IEEE Press, 2014.
[41]	ZHANG J , SINGH S . Visual-Lidar odometry and mapping:low-drift,robust,and fast[C]// Proceedings of the International Conference on Robotics and Automation.[S.l.:s.n.], 2015.
[42]	SHAO W , VIJAYARANNGAN S , LI C ,et al. Stereo visual inertial Lidar simultaneous localization and mapping[C]// Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems. Piscataway:IEEE Press, 2019.
[43]	ZUO X , GENEVA P , LEE W ,et al. LIC-Fusion:Lidar-inertial-camera odometry[C]// Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems. Piscataway:IEEE Press, 2019.
[44]	霍凤财, 迟金, 黄梓健 ,等. 移动机器人路径规划算法综述[J]. 吉林大学学报(信息科学版), 2018,36(6): 639-647.
	HUO F C , CHI J , HUANG Z J ,et al. Review of Path Planning for Mobile Robots[J]. Journal of Jilin University (Information Science Edition), 2018,36(6): 639-647.
[45]	JADERBERG M , MNIH V , MARIAN W ,et al. Reinforcement learning with unsupervised auxiliary tasks[C]// Proceedings of the 5th International Conference on Learning Representations.[S.l.:s.n.], 2017.
[46]	MIROWSKI P , PASCANU R , VIOLA F ,et al. Learning to navigate in complex environments[C]// Proceedings of the 5th International Conference on Learning Representations.[S.l.:s.n.], 2017.

面向防疫的智能导诊机器人关键技术及应用

Key technologies and applications of intelligent guiding robots for epidemic prevention

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