@article {1329501,
	title = {Toward Modular Soft Robotics: Proprioceptive Curvature Sensing and Sliding-Mode Control of Soft Bidirectional Bending Modules},
	journal = {Soft Robotics},
	volume = {4},
	number = {2},
	year = {2017},
	abstract = {Real-world environments are complex, unstructured, and often fragile. Soft robotics offers a solution for robots to safely interact with the environment and human coworkers, but suffers from a host of challenges in sensing and control of continuously deformable bodies. To overcome these challenges, this article considers a modular soft robotic architecture that offers proprioceptive sensing of pressure-operated bending actuation modules. We present integrated custom magnetic curvature sensors embedded in the neutral axis of bidirectional bending actuators. We describe our recent advances in the design and fabrication of these modules to improve the reliability of proprioceptive curvature feedback over our prior work. In particular, we study the effect of dimensional parameters on improving the linearity of curvature measurements. In addition, we present a sliding-mode controller formulation that drives the binary solenoid valve states directly, giving the control system the ability to hold the actuator steady without continuous pressurization and depressurization. In comparison to other methods, this control approach does not rely on pulse width modulation and hence offers superior dynamic performance (i.e., faster response rates). Our experimental results indicate that the proposed soft robotic modules offer a large range of bending angles with monotonic and more linear embedded curvature measurements, and that the direct sliding-mode control system exhibits improved bandwidth and a notable reduction in binary valve actuation operations compared to our earlier iterative sliding-mode controller.},
	author = {Ming Luo and Erik H. Skorina and Weijia Tao and Fuchen Chen and Selim Ozel and Yinan Sun and Cagdas D. Onal}
}
