PneuDrive: An Embedded Pressure Control System and Modeling Toolkit for Large-Scale Soft Robots
Curtis C. Johnson, Daniel G. Cheney, Dallin L. Cordon, Marc D. Killpack
TL;DR
To enable meter-scale soft robots, the authors introduce PneuDrive, a modular RS-485-based pressure-control system with embedded boards that support distributed, closed-loop control for many valves and high flow. The approach is complemented by a modeling toolkit offering three dynamic actuation models—linear, nonlinear, and parametric—along with Python-based parameter identification to support real-time simulation and control. Hardware demonstrations on a 1.16 m, 3-joint soft arm with 12 chambers show distributed control across multiple devices, achieving high loop rates and robust step/trajectory tracking. The work provides a principled framework for model selection and control design in large-scale soft robotics and releases open-source designs to accelerate research and development in this area.
Abstract
In this paper, we present a modular pressure control system called PneuDrive that can be used for large-scale, pneumatically-actuated soft robots. The design is particularly suited for situations which require distributed pressure control and high flow rates. Up to four embedded pressure control modules can be daisy-chained together as peripherals on a robust RS-485 bus, enabling closed-loop control of up to 16 valves with pressures ranging from 0-100 psig (0-689 kPa) over distances of more than 10 meters. The system is configured as a C++ ROS node by default. However, independent of ROS, we provide a Python interface with a scripting API for added flexibility. We demonstrate our implementation of PneuDrive through various trajectory tracking experiments for a three-joint, continuum soft robot with 12 different pressure inputs. Finally, we present a modeling toolkit with implementations of three dynamic actuation models, all suitable for real-time simulation and control. We demonstrate the use of this toolkit in customizing each model with real-world data and evaluating the performance of each model. The results serve as a reference guide for choosing between several actuation models in a principled manner. A video summarizing our results can be found here: https://bit.ly/3QkrEqO.
