No abstract

Soft robotic snakes promise significant advantages in achieving traveling curvature waves with a reduced number of active segments as well as allowing for safe and adaptive interaction with the environment and human users. However, current soft robot platforms suffer from a lack of accurate theoretical dynamic models and proprioceptive measurements, which impede advancements toward full autonomy. To address this gap, this paper details our recent results on the design, fabrication, and experimental evaluation of a new-generation pressure-operated soft robotic snake platform we call the WPI SRS, which employs custom magnetic sensors embedded in a flexible backbone to continuously monitor the curvature of each of its four bidirectional bending segments. In addition, we present a complete and accurate dynamic undulatory locomotion model that accounts for the propagation of frictional moments to describe linear and rotational motions of the SRS. Experimental studies indicate that on-board sensory measurements provide accurate real-time curvature feedback, and numerical simulations offer a level of abstraction for lateral undulation under ideal conditions.

Soft robotics carries the promise of making robots as capable and adaptable as biological creatures, but this will not be possible without the ability to perform self-sensing and control with precision and repeatability. In this paper, we seek to address this need with the development of a new pneumatically-actuated soft bending actuation module with integrated curvature sensing. We designed and fabricated two different versions of this module: One with a commercially available resistive flex sensor and the other with a magnetic curvature sensor of our own design, and used an external motion capture system to calibrate and validate these two approaches. In addition, we used an iterative sliding mode controller to drive the modules through step curvature references to demonstrate the controllability of the modules as well as compare the usability of the two sensors. We found that the magnetic sensor returned noisy but accurate data, while the flex sensor had minor inaccuracies and it was subject to overshoot but did not exhibit notable noise. Experimental results show that this phenomenon of overshoot from the flex sensor causes active feedback control of the bending actuator to exhibit significant positioning errors. This work demonstrates that our soft bending actuator can be controlled with repeatability and precision, and that our magnetic curvature sensor represents an improvement for use in proprioception and closed-loop control of soft robotic devices.

This paper addresses the design and dynamic analysis of a new generation of fluidic elastomer actuators (FEAs) that offer bidirectional bending developed as motion segments of a pressure-operated soft robotic snake. Our prior work on FEAs has identified a number of limitations, namely a high center of gravity, narrow base, slow dynamics, and a small range of pressure inputs. We developed two versions of FEAs based on an improved design concept with different geometric parameters and characterized their dynamic response under a custom visual tracking system. Compared with the previous actuators, the FEAs developed in this work offer robust operation, safety at larger input pressure values, faster response, lower center of gravity and a flat bottom for better compatibility for snake-like undulatory locomotion.

Soft pneumatic actuators provide many exciting properties, but controlling them without the use of bulky and expensive flow-control valves can be difficult and computationally expensive. We seek a solution to this problem by introducing an inexpensive and reliable muscle-like linear soft actuator used antagonistically to operate a rigid 1-DoF joint, resulting in a system that combines the advantages of rigid and soft robotics. Using this setup, we performed precise motion control using a sliding mode feedback controller as well as a sliding mode controller augmented by a feedforward term to modulate the state of solenoid valves that drive each actuator. We found that both controllers performed equivalently well in following a step function and in responding to a disturbance. The feedforward augmented controller performed significantly better when following dynamic trajectories over a range of frequencies and with the addition of an external force. The next step will be to modify our valve control scheme to allow for the determination of both the position and stiffness of the joint, better leveraging the advantages of soft pneumatic actuators.