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Thanks to their flexibility, soft robotic devices offer critical advantages over rigid robots, allowing adaptation to uncertainties in the environment. As such, soft robots enable various intriguing applications, including human-safe interaction devices, soft active rehabilitation devices, and soft grippers for pick-and-place tasks in industrial environments. In most cases, soft robots use pneumatic actuation to inflate the channels in a compliant material to obtain the movement of the structure. However, due to their flexibility and nonlinear behavior, as well as the compressibility of air, controlled movements of the soft robotic structure are difficult to attain. Obtaining physically-based mathematical models, which would enable the development of suitable control approaches for soft robots, constitutes thus a critical challenge in the field. The aim of this work is, therefore, to predict the movement of a pneumatic soft robot by using a data-driven approach based on the Koopman operator framework. The Koopman operator allows simplifying a nonlinear system by“lifting” its dynamics into a higher dimensional space, where its behavior can be accurately approximated by a linear model, thus allowing a significant reduction of the complexity of the design of the resulting controllers. 

The field of soft robotics is grounded on the idea that, due to their inherent compliance, soft robots can safely interact with the environment. Thus, the development of effective planning and control pipelines for soft robots should incorporate reliable robot-environment interaction models. This strategy enables soft robots to effectively exploit contacts to autonomously navigate and accomplish tasks in the environment. However, for a class of soft robots, namely vine-inspired, tip-extending or "vine" robots, such interaction models and the resulting planning and control strategies do not exist. In this paper, we analyze the behavior of vine robots interacting with their environment and propose an obstacle-interaction model that characterizes the bending and wrinkling deformation induced by the environment. Starting from this, we devise a novel obstacle-interaction planning method for these robots. We show how obstacle interactions can be effectively leveraged to enlarge the set of reachable workspace for the robot tip, and verify our findings with both simulated and real experiments. Our work improves the capabilities of this new class of soft robot, helping to advance the field of soft robotics. 

We present a pneumatic actuator capable of changing length by 1000%, applying both pushing and pulling forces, and independently modulating its length and stiffness. These characteristics are enabled by individually addressable internal and external chambers that work antagonistically against one another. The high deformation with low hysteresis is achieved by wrinkling of thin materials that are assumed to be inextensible but flexible, as opposed to stretchable. A model for the actuator is presented and validated with experimental results, showing capabilities of high strain, pushing and pulling, and independent control of length and stiffness. These charac- teristics are motivated by the application of a compliant truss robot. Accordingly, we show a simple grounded tetrahedron with three actuator elements and three static elements. We demonstrate motion of the tetrahedron apex against external loads and the ability of the structure to vary its stiffness. The actuator offers a unique set of characteristics that could increase the capabilities of soft robotic devices. 

For many types of robots, avoiding obstacles is necessary to prevent damage to the robot and environment. As a result, obstacle avoidance has historically been an im- portant problem in robot path planning and control. Soft robots represent a paradigm shift with respect to obstacle avoidance because their low mass and compliant bodies can make collisions with obstacles inherently safe. Here we consider the benefits of intentional obstacle collisions for soft robot navigation. We develop and experimentally verify a model of robot-obstacle interaction for a tip-extending soft robot. Building on the obstacle interaction model, we develop an algorithm to determine the path of a growing robot that takes into account obstacle collisions. We find that obstacle collisions can be beneficial for open-loop navigation of growing robots because the obstacles passively steer the robot, both reducing the uncertainty of the location of the robot and directing the robot to targets that do not lie on a straight path from the starting point. Our work shows that for a robot with predictable and safe interactions with obstacles, target locations in a cluttered, mapped environment can be reached reliably by simply setting the initial trajectory. This has implications for the control and design of robots with minimal active steering.