Subterranean burrowing is inherently difficult for robots because of the high forces experienced as well as the high amount of uncertainty in this domain. Because of the difficulty in modeling forces in granular media, we propose the use of a novel machine-learning control strategy to obtain optimal techniques for vertical self-burrowing. In this paper, we realize a snake-like bio-inspired robot that is equipped with an IMU and two triple-axis magnetometers. Utilizing magnetic field strength as an analog for depth, a novel deep learning architecture was proposed based on sinusoidal and random data in order to obtain a more efficient strategy for vertical self-burrowing. This strategy was able to outperform many other standard burrowing techniques and was able to automatically reach targeted burrowing depths. We hope these results will serve as a proof of concept for how optimization can be used to unlock the secrets of navigating in the subterranean world more efficiently.

This paper presents a soft earthworm robot that is capable of both efficient locomotion and obstacle avoidance. The robot is designed to replicate the unique locomotion mechanisms of earthworms, which enable them to move through narrow and complex environments with ease. The robot consists of multiple segments, each with its own set of actuators, that are connected through rigid plastic joints, allowing for increased adaptability and flexibility in navigating different environments. The robot utilizes proprioceptive sensing and control algorithms to detect and avoid obstacles in real-time while maintaining efficient locomotion. The robot uses a pneumatic actuation system to mimic the circumnutation behavior exhibited by plant roots in order to navigate through complex environments. The results demonstrate the capabilities of the robot for navigating through cluttered environments, making this development significant for various fields of robotics, including search and rescue, environmental monitoring, and medical procedures.

Soft actuators offer compliant and safe interaction with an unstructured environment compared to their rigid counterparts. However, control of these systems is often challenging because they are inherently under-actuated, have infinite degrees of freedom (DoF), and their mechanical properties can change by unknown external loads. Existing works mainly relied on discretization and reduction, suffering from either low accuracy or high computational cost for real-time control purposes. Recently, we presented an infinite-dimensional feedback controller for soft manipulators modeled by partial differential equations (PDEs) based on the Cosserat rod theory. In this study, we examine how to implement this controller in real-time using only a limited number of actuators. To do so, we formulate a convex quadratic programming problem that tunes the feedback gains of the controller in real-time such that it becomes realizable by the actuators. We evaluated the controller's performance through experiments on a physical soft robot capable of planar motions and show that the actual controller implemented by the finite-dimensional actuators still preserves the stabilizing property of the desired infinite-dimensional controller. This research fills the gap between the infinite-dimensional control design and finite-dimensional actuation in practice and suggests a promising direction for exploring PDE-based control design for soft robots.

In a geometric mechanics framework, the configuration space is decomposed into a shape space and a position space. The internal motion of the system is prescribed by a closed loop in the shape space, which causes net motion in the position space. If the shape space is a simply connected domain in an Euclidean space, then with an optimal choice of the body frame, the displacement in the position space is reasonably approximated by the surface integral of the height function, a functional relationship between the internal shape and position space variables. Our recent work has extended the scope of geometric methods from limbless undulatory system to those with legs; interestingly, the shape space for such systems has a torus structure. However, to the best of our knowledge, the optimal choice of the body frame on the torus shape space was not explored. In this paper, we develop a method to optimally choose the body frame on the torus which results in good approximation of displacement by the integral of the height function. We apply our methods to the centipede locomotion system and observe quantitative agreement of our prediction and experimental results.

Mobile robots of all shapes and sizes move through the air, water, and over ground. However, few robots can move through the ground. Not only are the forces resisting movement much greater than in air or water, but the interaction forces are more complicated. Here we propose a soft robotic device that burrows through dry sand while requiring an order of magnitude less force than a similarly sized intruding body. The device leverages the principles of both tip-extension and granular fluidization. Like roots, the device extends from its tip; the principle of tip-extension eliminates skin drag on the sides of the body, because the body is stationary with respect to the medium. We implement this with an everting, pressure-driven thin film body. The second principle, granular fluidization, enables a granular medium to adopt a dynamic fluid-like state when pressurized fluid is passed through it, reducing the forces acting on an object moving through it. We realize granular fluidization with a flow of air through the core of the body that mixes with the medium at the tip. The proposed device could lead to applications such as search and rescue in mudslides or shallow subterranean exploration. Further, because it creates a physical conduit with its body, electrical lines, fluids, or even tools could be passed through this channel.