用廉价传感器融合实现软体机器人长时间高精度形变感知。
An Enhanced Proprioceptive Method for Soft Robots Integrating Bend Sensors and IMUs
- IMU与弯折传感器融合，互相补偿漂移误差。
- 45分钟连续运行下均方根误差仅16.96mm，比纯IMU提升56%。
- 适合需要低成本、长期稳定感知的软体机器人应用。
本研究提出一种基于现成传感器的增强型本体感知方法，用于软体机器人精确形变估计，兼具低成本与易部署优势。通过将惯性测量单元（IMUs）与互补的弯折传感器结合，有效抑制了IMU漂移，实现可靠长时本体感知。采用卡尔曼滤波器对两类传感器提供的各段末端方向进行融合，实现相互补偿。基于分段恒定曲率模型，从融合方向数据中估计末端位置并重构机器人变形。在无负载、外部受力及被动障碍交互等条件下，连续运行45分钟的实验表明，均方根误差为16.96 mm（占总长度2.91%），相比纯IMU基准降低56%。结果表明，该方法不仅支持软体机器人长时间本体感知，且在多种复杂工况下仍保持高精度与鲁棒性。
原文摘要 · Abstract (English)
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.
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