In-Situ Sensing and Dynamics Predictions for Electrothermally-Actuated Soft Robot Limbs

TL;DR

This paper introduces an in-situ sensing and neural network-based modeling framework for electrothermally-actuated soft robot limbs, enabling accurate long-term motion predictions using temperature sensors and control inputs.

Contribution

It presents a novel integrated sensing and modeling approach for SMA-actuated soft robots, improving motion prediction accuracy and enabling practical, compact designs.

Findings

Temperature sensor data combined with control inputs predict robot motion over 10 minutes.

Prediction errors are comparable to deflection sensor accuracy.

The LSTM model effectively captures complex actuator dynamics.

Abstract

Untethered soft robots that locomote using electrothermally-responsive materials like shape memory alloy (SMA) face challenging design constraints for sensing actuator states. At the same time, modeling of actuator behaviors faces steep challenges, even with available sensor data, due to complex electrical-thermal-mechanical interactions and hysteresis. This article proposes a framework for in-situ sensing and dynamics modeling of actuator states, particularly temperature of SMA wires, which is used to predict robot motions. A planar soft limb is developed, actuated by a pair of SMA coils, that includes compact and robust sensors for temperature and angular deflection. Data from these sensors are used to train a neural network based on the long short-term memory (LSTM) architecture to model both unidirectional (single SMA) and bidirectional (both SMAs) motion. Predictions from the model demonstrate that data from the temperature sensor, combined with control inputs, allow for dynamics predictions over extraordinarily long open-loop timescales (10 minutes) with little drift. Prediction errors are on the order of the soft deflection sensor's accuracy. This architecture allows for compact designs of electrothermally-actuated soft robots that include sensing sufficient for motion predictions, helping to bring these robots into practical application.