Abstract
Modeling of soft robots is typically performed at the static level or at a second-order fully dynamic level. Controllers developed upon these models have several advantages and disadvantages. Static controllers, based on the kinematic relations tend to be the easiest to develop, but by sacrificing accuracy, efficiency and the natural dynamics. Controllers developed using second-order dynamic models tend to be computationally expensive, but allow optimal control. Here we propose that the dynamic model of a soft robot can be reduced to first-order dynamical equation owing to their high damping and low inertial properties, as typically observed in nature, with minimal loss in accuracy. This paper investigates the validity of this assumption and the advantages it provides to the modeling and control of soft robots. Our results demonstrate that this model approximation is a powerful tool for developing closed-loop task-space dynamic controllers for soft robots by simplifying the planning and sensory feedback process with minimal effects on the controller accuracy.
Highlights
Soft robotic technologies are becoming increasingly prevalent in the design and development of robots (Kim et al, 2013)
The learned models are derived using a kind of recurrent neural network called a nonlinear autoregressive exogenous (NARX) model (Billings, 2013)
This leads to dynamic behaviors which are well approximated by a first-order system, as typically observed in nature
Summary
Soft robotic technologies are becoming increasingly prevalent in the design and development of robots (Kim et al, 2013). Unlike static controllers, developing fully-dynamic controllers would involve a planning stage This has to be performed using some optimization techniques irrespective of the modeling strategy. A good example is the use of trajectory optimization for the control of a soft robotic manipulator using a model-based (Marchese et al, 2016) and model-free method (Thuruthel et al, 2017) This process is time consuming and debilitating for closed-loop dynamic control. It must be noted that unlike statespace dimensionality reduction methods (Thieffry et al, 2018), we are reducing the temporal dimensionality of the dynamic model Such a model reduction is based on the hypothesis that soft robots typically have high damping and low inertial properties. We present details on the learning architecture together with results of the model and the closed-loop task-space controller
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