发表机构
Cornell University; University of California San Diego（康奈尔大学; 加利福尼亚大学圣迭戈分校）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对受鳗鱼启发的软体游泳机器人，本文提出REACH算法，结合软体机器人模型、sigma点滤波器和统计假设检验，可在不同传感器配置、游动步态下准确估计执行器健康，实验验证其在噪声数据和制造差异下的有效性。
AI 中文摘要
本文开发了一种可进行鳗形游动的软体游泳机器人的执行器健康估计算法。由于水下机器人的作业环境恶劣，且软体机器人材料和执行器易出现性能退化，为使机器人在执行器退化或故障时仍能完成任务并返回基地，准确估计执行器功能十分必要。该架构被命名为REACH（Real-time Estimator of Actuator Control and Health，执行器控制与健康实时估计器），它采用软体机器人模型、sigma点滤波器和形式化统计假设检验，以充分捕捉非线性特性和随时间的变化。本文对比了使用三种传感器（GPS、IMU和弯曲传感器）的REACH性能，每种执行器配备一个传感器，结果显示弯曲传感器和IMU均为合适选择；还评估了IMU和弯曲传感器的数量与布置，表明IMU配备两个传感器即可满足需求，而弯曲传感器则需要三个。本文对比了三种游动步态（直线游动、大角度转弯、小角度转弯），结果显示REACH可成功预测三种步态下的执行器健康状况，性能差异极小。滤波器验证方法表明，该故障估计算法在查找正确退化情况时具有统计一致性。本文使用从鱼形机器人收集的弯曲传感器数据进行实验评估，结果显示REACH可在存在噪声数据和制造差异的情况下成功估计执行器健康状况。
英文摘要
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.
CommentsCorrected some minor typos
Journal ref2025 IEEE 8th International Conference on Soft Robotics (RoboSoft), 2025, pp. 297-302
DOI：10.1109/robosoft63089.2025.11020927
