Constant Force-Tracking Control Based on Deep Reinforcement Learning in Dynamic Auscultation Environment
Intelligent medical robots can effectively help doctors carry out a series of medical diagnoses and auxiliary treatments and alleviate the current shortage of social personnel. Therefore, this paper investigates how to use deep reinforcement learning to solve dynamic medical auscultation tasks. We p...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-02-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/23/4/2186 |
| _version_ | 1797618202104037376 |
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| author | Tieyi Zhang Chao Chen Minglei Shu Ruotong Wang Chong Di Gang Li |
| author_facet | Tieyi Zhang Chao Chen Minglei Shu Ruotong Wang Chong Di Gang Li |
| author_sort | Tieyi Zhang |
| collection | DOAJ |
| description | Intelligent medical robots can effectively help doctors carry out a series of medical diagnoses and auxiliary treatments and alleviate the current shortage of social personnel. Therefore, this paper investigates how to use deep reinforcement learning to solve dynamic medical auscultation tasks. We propose a constant force-tracking control method for dynamic environments and a modeling method that satisfies physical characteristics to simulate the dynamic breathing process and design an optimal reward function for the task of achieving efficient learning of the control strategy. We have carried out a large number of simulation experiments, and the error between the tracking of normal force and expected force is basically within ±0.5 N. The control strategy is tested in a real environment. The preliminary results show that the control strategy performs well in the constant force-tracking of medical auscultation tasks. The contact force is always within a safe and stable range, and the average contact force is about 5.2 N. |
| first_indexed | 2024-03-11T08:10:45Z |
| format | Article |
| id | doaj.art-5b5be290dad443df89d5bc2d3dc86b21 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-11T08:10:45Z |
| publishDate | 2023-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-5b5be290dad443df89d5bc2d3dc86b212023-11-16T23:11:25ZengMDPI AGSensors1424-82202023-02-01234218610.3390/s23042186Constant Force-Tracking Control Based on Deep Reinforcement Learning in Dynamic Auscultation EnvironmentTieyi Zhang0Chao Chen1Minglei Shu2Ruotong Wang3Chong Di4Gang Li5School of Mathematics and Statistics, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250353, ChinaSchool of Mathematics and Statistics, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250353, ChinaSchool of Mathematics and Statistics, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250353, ChinaShandong Artificial Intelligence Institute, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250014, ChinaShandong Artificial Intelligence Institute, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250014, ChinaSchool of Mathematics and Statistics, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250353, ChinaIntelligent medical robots can effectively help doctors carry out a series of medical diagnoses and auxiliary treatments and alleviate the current shortage of social personnel. Therefore, this paper investigates how to use deep reinforcement learning to solve dynamic medical auscultation tasks. We propose a constant force-tracking control method for dynamic environments and a modeling method that satisfies physical characteristics to simulate the dynamic breathing process and design an optimal reward function for the task of achieving efficient learning of the control strategy. We have carried out a large number of simulation experiments, and the error between the tracking of normal force and expected force is basically within ±0.5 N. The control strategy is tested in a real environment. The preliminary results show that the control strategy performs well in the constant force-tracking of medical auscultation tasks. The contact force is always within a safe and stable range, and the average contact force is about 5.2 N.https://www.mdpi.com/1424-8220/23/4/2186constant force-trackingdeep reinforcement learningauscultation robotcompliant control |
| spellingShingle | Tieyi Zhang Chao Chen Minglei Shu Ruotong Wang Chong Di Gang Li Constant Force-Tracking Control Based on Deep Reinforcement Learning in Dynamic Auscultation Environment Sensors constant force-tracking deep reinforcement learning auscultation robot compliant control |
| title | Constant Force-Tracking Control Based on Deep Reinforcement Learning in Dynamic Auscultation Environment |
| title_full | Constant Force-Tracking Control Based on Deep Reinforcement Learning in Dynamic Auscultation Environment |
| title_fullStr | Constant Force-Tracking Control Based on Deep Reinforcement Learning in Dynamic Auscultation Environment |
| title_full_unstemmed | Constant Force-Tracking Control Based on Deep Reinforcement Learning in Dynamic Auscultation Environment |
| title_short | Constant Force-Tracking Control Based on Deep Reinforcement Learning in Dynamic Auscultation Environment |
| title_sort | constant force tracking control based on deep reinforcement learning in dynamic auscultation environment |
| topic | constant force-tracking deep reinforcement learning auscultation robot compliant control |
| url | https://www.mdpi.com/1424-8220/23/4/2186 |
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