Real-time Estimator of Actuator Control and Health (REACH) on an Eel-Inspired Soft Robot

2026-08-14

AI Summary

This study addresses the challenge of real-time monitoring for actuator degradation and faults in underwater soft robots by proposing the REACH framework. Integrating soft body dynamics modeling, Sigma-point filtering, and formal statistical hypothesis testing, this approach enables real-time estimation of actuator health states. Experimental validation confirms the efficacy of the sensor configuration strategy and demonstrates statistical consistency in fault estimation across multiple gaits. Results indicate that the framework accurately assesses actuator health despite noise interference and manufacturing variability, establishing the reliability of combined IMU and bending sensor configurations. Consequently, this work provides a novel paradigm for robust perception in soft robotics, offering a validated solution for maintaining operational integrity in unstructured underwater environments through rigorous model-based state estimation and sensor fusion techniques.

Abstract

An actuator health estimation algorithm for a soft swimming robot that can perform anguilliform swimming is developed. Due to harsh operational environments of underwater robots, and the common degradation of soft robot materials and actuators, accurate estimation of actuator functionality is necessary for robots to perform their missions as well as return to base in the event of actuator degradation and failure. Termed REACH (Real-time Estimator of Actuator Control and Health), the architecture employs a soft robot model, sigma point filter, and a formal statistical hypothesis test to adequately capture the nonlinearities and changes over time. The performance of REACH using three sensor types (GPS, IMU, and Bend Sensor) with one sensor on each actuator is compared, demonstrating that both bend sensor and IMU are adequate choices. Sensor quantity and placement are evaluated for IMU and bend sensor, showing two sensors are sufficient for IMU, whereas three sensors are needed for bend sensor. Three swimming gaits (linear swimming, wide turning, tight turning) are compared, demonstrating that REACH can successfully predict actuator health for all three gaits, with minimal differences in performance. A filter validation method shows the fault estimation algorithm is statistically consistent in finding the correct degradation. The approach is experimentally evaluated using bend sensor data collected from a fish robot, demonstrating that REACH can successfully estimate actuator health with noisy data and variations in manufacturing.

Problem

• Soft Robot
• Actuator Health Estimation
• Underwater Robot
• Fault Detection

Innovation

• Actuator Health Estimation
• Soft Robot
• Sigma Point Filter
• Statistical Hypothesis Test
• REACH