A Hybrid Adaptive Controller for Soft Robot Interchangeability

TL;DR

This paper introduces a hybrid adaptive control system for soft robots that enhances interchangeability and performance across different robot configurations using neural networks and online optimization.

Contribution

It presents a novel hybrid control approach combining offline-trained neural networks with online kinematic optimization for soft robot interchangeability.

Findings

Achieved trajectory following errors of 3.3% and 4.3% in experiments.

Demonstrated robustness across different control frequencies and velocities.

Showed improved performance over traditional model-based controllers in simulations.

Abstract

Soft robots have been leveraged in considerable areas like surgery, rehabilitation, and bionics due to their softness, flexibility, and safety. However, it is challenging to produce two same soft robots even with the same mold and manufacturing process owing to the complexity of soft materials. Meanwhile, widespread usage of a system requires the ability to replace inner components without highly affecting system performance, which is interchangeability. Due to the necessity of this property, a hybrid adaptive controller is introduced to achieve interchangeability from the perspective of control approaches. This method utilizes an offline-trained recurrent neural network controller to cope with the nonlinear and delayed response from soft robots. Furthermore, an online optimizing kinematics controller is applied to decrease the error caused by the above neural network controller. Soft pneumatic robots with different deformation properties but the same mold have been included for validation experiments. In the experiments, the systems with different actuation configurations and the different robots follow the desired trajectory with errors of 3.3 +- 2.9% and 4.3 +- 4.1% compared with the working space length, respectively. Such an adaptive controller also shows good performance on different control frequencies and desired velocities. This controller is also compared with a model-based controller in simulation. This controller endows soft robots with the potential for wide application, and future work may include different offline and online controllers. A weight parameter adjusting strategy may also be proposed in the future.