Versatile graceful degradation framework for bio-inspired proprioception with redundant soft sensors
Reliable proprioception and feedback from soft sensors are crucial for enabling soft robots to function intelligently in real-world environments. Nevertheless, soft sensors are fragile and are susceptible to various damage sources in such environments. Some researchers have utilized redundant config...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2025-01-01
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| Series: | Frontiers in Robotics and AI |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/frobt.2024.1504651/full |
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| author | Taku Sugiyama Kyo Kutsuzawa Dai Owaki Elijah Almanzor Fumiya Iida Mitsuhiro Hayashibe |
| author_facet | Taku Sugiyama Kyo Kutsuzawa Dai Owaki Elijah Almanzor Fumiya Iida Mitsuhiro Hayashibe |
| author_sort | Taku Sugiyama |
| collection | DOAJ |
| description | Reliable proprioception and feedback from soft sensors are crucial for enabling soft robots to function intelligently in real-world environments. Nevertheless, soft sensors are fragile and are susceptible to various damage sources in such environments. Some researchers have utilized redundant configuration, where healthy sensors compensate instantaneously for lost ones to maintain proprioception accuracy. However, achieving consistently reliable proprioception under diverse sensor degradation remains a challenge. This paper proposes a novel framework for graceful degradation in redundant soft sensor systems, incorporating a stochastic Long Short-Term Memory (LSTM) and a Time-Delay Feedforward Neural Network (TDFNN). The LSTM estimates readings from healthy sensors to compare them with actual data. Then, statistically abnormal readings are zeroed out. The TDFNN receives the processed sensor readings to perform proprioception. Simulation experiments with a musculoskeletal leg that contains 40 nonlinear soft sensors demonstrate the effectiveness of the proposed framework. Results show that the knee angle proprioception accuracy is retained across four distinct degradation scenarios. Notably, the mean proprioception error increases by less than 1.91°(1.36%) when 30% of the sensors are degraded. These results suggest that the proposed framework enhances the reliability of soft sensor proprioception, thereby improving the robustness of soft robots in real-world applications. |
| format | Article |
| id | doaj-art-18a63ea6385b41d4a1f173c0e8d2d37c |
| institution | Kabale University |
| issn | 2296-9144 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Robotics and AI |
| spelling | doaj-art-18a63ea6385b41d4a1f173c0e8d2d37c2025-01-06T05:13:11ZengFrontiers Media S.A.Frontiers in Robotics and AI2296-91442025-01-011110.3389/frobt.2024.15046511504651Versatile graceful degradation framework for bio-inspired proprioception with redundant soft sensorsTaku Sugiyama0Kyo Kutsuzawa1Dai Owaki2Elijah Almanzor3Fumiya Iida4Mitsuhiro Hayashibe5Neuro-robotics Laboratory, Department of Robotics, Graduate School of Engineering, Tohoku University, Sendai, JapanNeuro-robotics Laboratory, Department of Robotics, Graduate School of Engineering, Tohoku University, Sendai, JapanNeuro-robotics Laboratory, Department of Robotics, Graduate School of Engineering, Tohoku University, Sendai, JapanBio-Inspired Robotics Laboratory, Department of Engineering, University of Cambridge, Cambridge, United KingdomBio-Inspired Robotics Laboratory, Department of Engineering, University of Cambridge, Cambridge, United KingdomNeuro-robotics Laboratory, Department of Robotics, Graduate School of Engineering, Tohoku University, Sendai, JapanReliable proprioception and feedback from soft sensors are crucial for enabling soft robots to function intelligently in real-world environments. Nevertheless, soft sensors are fragile and are susceptible to various damage sources in such environments. Some researchers have utilized redundant configuration, where healthy sensors compensate instantaneously for lost ones to maintain proprioception accuracy. However, achieving consistently reliable proprioception under diverse sensor degradation remains a challenge. This paper proposes a novel framework for graceful degradation in redundant soft sensor systems, incorporating a stochastic Long Short-Term Memory (LSTM) and a Time-Delay Feedforward Neural Network (TDFNN). The LSTM estimates readings from healthy sensors to compare them with actual data. Then, statistically abnormal readings are zeroed out. The TDFNN receives the processed sensor readings to perform proprioception. Simulation experiments with a musculoskeletal leg that contains 40 nonlinear soft sensors demonstrate the effectiveness of the proposed framework. Results show that the knee angle proprioception accuracy is retained across four distinct degradation scenarios. Notably, the mean proprioception error increases by less than 1.91°(1.36%) when 30% of the sensors are degraded. These results suggest that the proposed framework enhances the reliability of soft sensor proprioception, thereby improving the robustness of soft robots in real-world applications.https://www.frontiersin.org/articles/10.3389/frobt.2024.1504651/fullsoft sensors and actuatorsredundant sensorsneural networkself-adaptationproprioceptiongraceful degradation |
| spellingShingle | Taku Sugiyama Kyo Kutsuzawa Dai Owaki Elijah Almanzor Fumiya Iida Mitsuhiro Hayashibe Versatile graceful degradation framework for bio-inspired proprioception with redundant soft sensors Frontiers in Robotics and AI soft sensors and actuators redundant sensors neural network self-adaptation proprioception graceful degradation |
| title | Versatile graceful degradation framework for bio-inspired proprioception with redundant soft sensors |
| title_full | Versatile graceful degradation framework for bio-inspired proprioception with redundant soft sensors |
| title_fullStr | Versatile graceful degradation framework for bio-inspired proprioception with redundant soft sensors |
| title_full_unstemmed | Versatile graceful degradation framework for bio-inspired proprioception with redundant soft sensors |
| title_short | Versatile graceful degradation framework for bio-inspired proprioception with redundant soft sensors |
| title_sort | versatile graceful degradation framework for bio inspired proprioception with redundant soft sensors |
| topic | soft sensors and actuators redundant sensors neural network self-adaptation proprioception graceful degradation |
| url | https://www.frontiersin.org/articles/10.3389/frobt.2024.1504651/full |
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