Design, Analysis, and Real-Time Simulation of a 3D Soft Robotic Snake.

Snakes are a remarkable source of inspiration for mobile search-and-rescue robots. Their unique slender body structure and multiple modes of locomotion are well-suited to movement in narrow passages and other difficult terrain. The design, manufacturing, modeling, and control techniques of soft robotics make it possible to imitate the structure, mechanical properties, and locomotion gaits of snakes, opening up new possibilities in robotics research. Building on our track record of contributions in this area, this article presents a soft robotic snake made of modules that can actively deform in three-dimensional (3D) and rigorously studies its performance under a range of conditions, including gait parameters, number of modules, and differences in the environment. A soft 3D-printed wave spring sheath is developed to support the robot modules, increasing the snake's performance in climbing steps threefold. Finally, we introduce a simulator and a numerical model to provide a real-time simulation of the soft robotic snake. With the help of the real-time simulator, it is possible to develop and test new locomotion gaits for the soft robotic snake within a short period of time, compared with experimental trial and error. As a result, the soft robotic snake presented in this article is able to locomote on different surfaces, perform different bioinspired and custom gaits, and climb over steps.

Motion Planning and Iterative Learning Control of a Modular Soft Robotic Snake.

Snake robotics is an important research topic with a wide range of applications, including inspection in confined spaces, search-and-rescue, and disaster response. Snake robots are well-suited to these applications because of their versatility and adaptability to unstructured and constrained environments. In this paper, we introduce a soft pneumatic robotic snake that can imitate the capabilities of biological snakes, its soft body can provide flexibility and adaptability to the environment. This paper combines soft mobile robot modeling, proprioceptive feedback control, and motion planning to pave the way for functional soft robotic snake autonomy. We propose a pressure-operated soft robotic snake with a high degree of modularity that makes use of customized embedded flexible curvature sensing. On this platform, we introduce the use of iterative learning control using feedback from the on-board curvature sensors to enable the snake to automatically correct its gait for superior locomotion. We also present a motion planning and trajectory tracking algorithm using an adaptive bounding box, which allows for efficient motion planning that still takes into account the kinematic state of the soft robotic snake. We test this algorithm experimentally, and demonstrate its performance in obstacle avoidance scenarios.

Reverse pneumatic artificial muscles (rPAMs): Modeling, integration, and control.

Despite offering many advantages over traditional rigid actuators, soft pneumatic actuators suffer from a lack of comprehensive, computationally efficient models and precise embedded control schemes without bulky flow-control valves and extensive computer hardware. In this article, we consider an inexpensive and reliable soft linear actuator, called the reverse pneumatic artificial muscle (rPAM), which consists of silicone rubber that is radially constrained by symmetrical double-helix threading. We describe analytical and numerical static models of this actuator, and compare their performance against experimental results. To study the application of rPAMs to operate underlying kinematic linkage skeletons, we consider a single degree-of-freedom revolute joint that is driven antagonistically by two of these actuators. An analytical model is then derived, and its accuracy in predicting the static joint angle as a function of input pressures is presented. Using this analytical model, we perform dynamic characterization of this system. Finally, we propose a sliding-mode controller, and a sliding mode controller augmented by a feed-forward term to modulate miniature solenoid valves that control air flow to each actuator. Experiments show that both controllers function well, while the feed-forward term improves the performance of the controller following dynamic trajectories.

A Soft Robotic Wearable Wrist Device for Kinesthetic Haptic Feedback.

Advances in soft robotics provide a unique approach for delivering haptic feedback to a user by a soft wearable device. Such devices can apply forces directly on the human joints, while still maintaining the safety and flexibility necessary for use in close proximity to the human body. To take advantage of these properties, we present a new haptic wrist device using pressure-driven soft actuators called reverse pneumatic artificial muscles (rPAMs) mounted on four sides of the wrist. These actuators are originally pre-strained and release compressive stress under pressure, applying a safe torque around the wrist joints while being compact and portable, representing the first soft haptic device capable of real-time feedback. To demonstrate the functional utility of this device, we created a virtual path-following task, wherein the user employs the motion of their wrist to control their embodied agent. We used the haptic wrist device to assist the user in following the path and study their performance with and without haptic feedback in multiple scenarios. Our results quantify the effect of wearable soft robotic haptic feedback on user performance. Specifically, we observed that our haptic feedback system improved the performance of users following complicated paths in a statistically significant manner, but did not show improvement for simple linear paths. Based on our findings, we anticipate broader applications of wearable soft robotic haptic devices toward intuitive user interactions with robots, computers, and other users.

Toward Modular Soft Robotics: Proprioceptive Curvature Sensing and Sliding-Mode Control of Soft Bidirectional Bending Modules.

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.

Slithering towards autonomy: a self-contained soft robotic snake platform with integrated curvature sensing.

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.