An Enhanced Proprioceptive Method for Soft Robots Integrating Bend Sensors and IMUs

2025-11-02

AI Summary

To address the challenges of low accuracy, severe drift, and high cost in long-term shape estimation for soft robots, this paper proposes a proprioceptive method leveraging off-the-shelf bend sensors and inertial measurement units (IMUs). A Kalman filter-based fusion framework is designed to enable mutual compensation between IMU and bend sensor measurements, integrated with a piecewise constant curvature model to achieve continuous, self-powered deformation reconstruction. The approach effectively suppresses IMU integration drift, maintaining high stability over 45 minutes of continuous operation, with a root-mean-square error of only 16.96 mm (2.91% of total length)—a 56% reduction compared to single-sensor baselines. Experimental results demonstrate the feasibility of long-term, high-accuracy shape estimation without custom hardware, offering a low-cost, easily deployable solution. This work establishes a reliable perception foundation for the practical deployment of soft robots.

Technology Category

• Intelligent Robots: State Estimation
• Computer Vision: Low Level & Physics-based Vision
• Planning, Routing, and Scheduling: Replanning and Plan Repair

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation

Abstract

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.

Problem

• Accurately estimating soft robot shape with cost-effective sensors
• Mitigating IMU drift through complementary bend sensor integration
• Improving shape reconstruction robustness under diverse operating conditions

Innovation

• Integrating IMUs with bend sensors for drift mitigation
• Using Kalman filter for sensor fusion in orientation
• Applying piecewise constant curvature model for shape reconstruction