Evaluating Accuracy of Vine Robot Shape Sensing with Distributed Inertial Measurement Units

Soft, growing vine robots are well-suited for urban search and rescue operations due to their ability to navigate confined and cluttered spaces by extending from the tip without disturbing surrounding debris, enabling access to areas that are otherwise unreachable. A key challenge in such environments is localizing the robot’s tip—critical for locating trapped survivors—especially where GPS and line-of-sight systems are unavailable. Shape sensing using distributed inertial measurement units (IMUs) along the robot body provides a viable solution for estimating the robot’s configuration in real time.

IMUs measure orientation through accelerometers, gyroscopes, and magnetometers. While accelerometers provide accurate tilt measurements relative to gravity, the heading (yaw) estimation relies heavily on magnetometers, which are prone to errors from magnetic interference and require recalibration when environmental magnetic fields change. In one study, the BNO055 IMU used exhibited a magnetometer heading accuracy of ±2.5°, but time-varying magnetic interference increased orientation error up to 9° about one axis. Additionally, IMUs are susceptible to drift over time; in an 18-minute drift test, three out of 18 IMUs showed significant drift and were excluded from shape estimation.

To reconstruct shape from IMU data, models such as the piecewise constant curvature (PCC) and circular arc assumptions are used. The PCC model, combined with quaternion-based orientation data from IMUs, enables accurate shape reconstruction and supports closed-loop control, with reported accuracy reaching 93% of the robot’s length in experimental validation. A more recent method fuses local strain measurements from fiber Bragg grating sensors with global pose data from IMUs to minimize spatial error accumulation, demonstrating higher accuracy and robustness compared to conventional PCC methods.

In field-ready vine robots, a circular arc model was tested under passive and active steering conditions. During passive steering (0°–90°), the maximum tip position error was 14.7%, while under active steering with pouch motor pressure varying from 0–2.15 PSI, the error increased to 20.5%. These results highlight the influence of actuation and environmental interaction on sensing accuracy.

For deployment in debris-filled environments, sensor robustness is critical. Flexible sensor tapes with embedded IMUs allow real-time localization and are scalable and modular, supporting integration with various sensing modalities such as temperature and tactile feedback. The embeddable nature of IMUs eliminates reliance on external infrastructure like optical or electromagnetic tracking systems, expanding operational scenarios.

Future improvements include enhancing length estimation in curvature models, incorporating redundant IMUs per sensor band to improve accuracy, and refining calibration to mitigate drift and magnetic interference. These advances support robust proprioception in soft robots, a key enabler for autonomous navigation and AI-driven perception in unstructured, GPS-denied environments such as collapsed structures.

This research investigates the critical challenge of accurate shape sensing and localization for vine robots operating within constrained, debris-filled environments. As soft continuum robots grow through complex terrains, traditional external tracking methods often fail due to occlusion and the lack of line-of-sight. The authors propose a proprioceptive sensing strategy utilizing distributed Inertial Measurement Units (IMUs) embedded along the robot's body, complemented by force sensing capabilities to detect environmental interactions. This approach shifts the reliance from external perception to internal state estimation, allowing the robot to "feel" its own configuration and the obstacles it encounters.

A key contribution of this work is the integration of inertial data with contact-based constraints to mitigate the inherent drift associated with IMU dead reckoning. By identifying points of contact with debris via force sensors, the system can anchor specific segments of the robot, thereby refining the overall shape reconstruction and kinematic estimation. The study systematically evaluates the accuracy of this distributed sensing approach, demonstrating how sensor fusion techniques can effectively resolve the robot's configuration even when visual feedback is unavailable or unreliable.

This work is significant to the broader field of embodied AI and robotics as it advances the perceptual capabilities of soft, actuated systems. Enabling robust proprioception in unstructured environments is essential for autonomous navigation and manipulation tasks, particularly in search-and-rescue scenarios where robots must traverse rubble or confined spaces. By establishing reliable methods for internal state estimation, this research lays the groundwork for more intelligent and adaptive soft robots capable of operating independently in the real world.

Summary of "Evaluating Accuracy of Vine Robot Shape Sensing with Distributed Inertial Measurement Units"

This paper presents a method for shape sensing in soft vine robots using distributed inertial measurement units (IMUs) and force sensing, with applications in debris-filled or unstructured environments. The work addresses a critical challenge in embodied AI: enabling robots to accurately perceive their own configuration despite deformations and external disturbances. By integrating IMU data with force feedback, the authors propose a localized sensing framework that improves pose estimation and shape reconstruction, even in cluttered or partially occluded scenarios. The approach leverages the robot's inherent flexibility, making it well-suited for navigation in tight or irregular spaces where rigid robots would fail.

The key contributions include a novel calibration and fusion technique for IMU data, which accounts for sensor noise and dynamic deformations, and an evaluation of accuracy under varying environmental conditions. The paper demonstrates that this distributed sensing approach outperforms traditional centralized methods, particularly in scenarios with partial sensor occlusion or debris interference. This work is significant for AI-driven robotics, as it advances the capability of soft robots to operate autonomously in complex, real-world settings—such as search-and-rescue, exploration, or medical applications—where robust perception is essential. The insights highlight the potential of hybrid sensing (IMU + force) to enhance embodied intelligence in non-rigid robotic systems.