An Enhanced Proprioceptive Method for Soft Robots Integrating Bend Sensors and IMUs
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arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
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| _version_ | 1866908624839770112 |
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| author | Han, Dong Heon Mehta, Mayank Zuo, Runze Wanger, Zachary Bruder, Daniel |
| author_facet | Han, Dong Heon Mehta, Mayank Zuo, Runze Wanger, Zachary Bruder, Daniel |
| contents | This study presents an enhanced proprioceptive method for accurate shape estimation of soft robots using only off-the-shelf sensors, ensuring cost-effectiveness and easy applicability. By integrating inertial measurement units (IMUs) with complementary bend sensors, IMU drift is mitigated, enabling reliable long-term proprioception. A Kalman filter fuses segment tip orientations from both sensors in a mutually compensatory manner, improving shape estimation over single-sensor methods. A piecewise constant curvature model estimates the tip location from the fused orientation data and reconstructs the robot's deformation. Experiments under no loading, external forces, and passive obstacle interactions during 45 minutes of continuous operation showed a root mean square error of 16.96 mm (2.91% of total length), a 56% reduction compared to IMU-only benchmarks. These results demonstrate that our approach not only enables long-duration proprioception in soft robots but also maintains high accuracy and robustness across these diverse conditions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_01165 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | An Enhanced Proprioceptive Method for Soft Robots Integrating Bend Sensors and IMUs Han, Dong Heon Mehta, Mayank Zuo, Runze Wanger, Zachary Bruder, Daniel Robotics This study presents an enhanced proprioceptive method for accurate shape estimation of soft robots using only off-the-shelf sensors, ensuring cost-effectiveness and easy applicability. By integrating inertial measurement units (IMUs) with complementary bend sensors, IMU drift is mitigated, enabling reliable long-term proprioception. A Kalman filter fuses segment tip orientations from both sensors in a mutually compensatory manner, improving shape estimation over single-sensor methods. A piecewise constant curvature model estimates the tip location from the fused orientation data and reconstructs the robot's deformation. Experiments under no loading, external forces, and passive obstacle interactions during 45 minutes of continuous operation showed a root mean square error of 16.96 mm (2.91% of total length), a 56% reduction compared to IMU-only benchmarks. These results demonstrate that our approach not only enables long-duration proprioception in soft robots but also maintains high accuracy and robustness across these diverse conditions. |
| title | An Enhanced Proprioceptive Method for Soft Robots Integrating Bend Sensors and IMUs |
| topic | Robotics |
| url | https://arxiv.org/abs/2511.01165 |