A study on robot force control based on the GMM/GMR algorithm fusing different compensation strategies
Copyright © 2024 Xiao, Zhang, Zhang, Chen, Zou and Wu..
To address traditional impedance control methods' difficulty with obtaining stable forces during robot-skin contact, a force control based on the Gaussian mixture model/Gaussian mixture regression (GMM/GMR) algorithm fusing different compensation strategies is proposed. The contact relationship between a robot end effector and human skin is established through an impedance control model. To allow the robot to adapt to flexible skin environments, reinforcement learning algorithms and a strategy based on the skin mechanics model compensate for the impedance control strategy. Two different environment dynamics models for reinforcement learning that can be trained offline are proposed to quickly obtain reinforcement learning strategies. Three different compensation strategies are fused based on the GMM/GMR algorithm, exploiting the online calculation of physical models and offline strategies of reinforcement learning, which can improve the robustness and versatility of the algorithm when adapting to different skin environments. The experimental results show that the contact force obtained by the robot force control based on the GMM/GMR algorithm fusing different compensation strategies is relatively stable. It has better versatility than impedance control, and the force error is within ~±0.2 N.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2024 |
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| Erschienen: |
2024 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:18 |
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| Enthalten in: |
Frontiers in neurorobotics - 18(2024) vom: 30., Seite 1290853 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Xiao, Meng [VerfasserIn] |
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| Links: |
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| Themen: |
Deep Q-network (DQN) |
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| Anmerkungen: |
Date Revised 14.02.2024 published: Electronic-eCollection Citation Status PubMed-not-MEDLINE |
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| doi: |
10.3389/fnbot.2024.1290853 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM368381471 |
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| 245 | 1 | 2 | |a A study on robot force control based on the GMM/GMR algorithm fusing different compensation strategies |
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| 520 | |a Copyright © 2024 Xiao, Zhang, Zhang, Chen, Zou and Wu. | ||
| 520 | |a To address traditional impedance control methods' difficulty with obtaining stable forces during robot-skin contact, a force control based on the Gaussian mixture model/Gaussian mixture regression (GMM/GMR) algorithm fusing different compensation strategies is proposed. The contact relationship between a robot end effector and human skin is established through an impedance control model. To allow the robot to adapt to flexible skin environments, reinforcement learning algorithms and a strategy based on the skin mechanics model compensate for the impedance control strategy. Two different environment dynamics models for reinforcement learning that can be trained offline are proposed to quickly obtain reinforcement learning strategies. Three different compensation strategies are fused based on the GMM/GMR algorithm, exploiting the online calculation of physical models and offline strategies of reinforcement learning, which can improve the robustness and versatility of the algorithm when adapting to different skin environments. The experimental results show that the contact force obtained by the robot force control based on the GMM/GMR algorithm fusing different compensation strategies is relatively stable. It has better versatility than impedance control, and the force error is within ~±0.2 N | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Gaussian mixture model/Gaussian mixture regression (GMM/GMR) | |
| 650 | 4 | |a deep Q-network (DQN) | |
| 650 | 4 | |a impedance control | |
| 650 | 4 | |a reinforcement learning | |
| 650 | 4 | |a robot force control | |
| 700 | 1 | |a Zhang, Xuefei |e verfasserin |4 aut | |
| 700 | 1 | |a Zhang, Tie |e verfasserin |4 aut | |
| 700 | 1 | |a Chen, Shouyan |e verfasserin |4 aut | |
| 700 | 1 | |a Zou, Yanbiao |e verfasserin |4 aut | |
| 700 | 1 | |a Wu, Wen |e verfasserin |4 aut | |
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