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Due to their compliant mechanics, many soft and flexible robots are well-suited to operate in confined spaces that challenge traditional legged and wheeled robots. In particular, undulatory and peristaltic robots excel in enclosed tunnels and pipes, as their locomotion methods and compliance allow them to wriggle and squeeze through narrowings and gaps. However, these environments frequently contain features that are impassable with passive deformation alone, such as sharp turns and T-junctions. In this work, we developed the first closed-loop, peristaltic turning algorithm that scales turning angle based on contact force feedback in confined spaces. We verified the controller’s effectiveness using a dynamic model of a soft worm robot as our test platform. The model was sent through pipe bends with varying radii of curvature, diameters, and geometries, using both open-loop and closed-loop controllers. The different turning strategies were compared using speed and the cost of transport (COT) as the primary metrics. Our adaptive algorithm was the only turning method which maintained high speed performance across all bends tested. The model averaged just a 1.6% loss in speed compared to the best fixed turning angle strategy for each pipe curvature. Adaptive turning also allowed the model to traverse small radius of curvature (ROC) bends (0.5 and 0.6 m) in which the model became stuck when using straightline locomotion. Furthermore, of the controllers we tested, adaptive turning offered the lowest COT in most pipe bend geometries. We also showed that the controller could autonomously transition from left to right turns (and vice versa) and adjust to a spiral turn with a continuously changing ROC. A worm robot with this controller, these sensors, and turning capabilities would be well equipped for operation in a wide variety of unstructured and confined environments. 

Biological inspiration offers new and innovative solutions to exploring challenging terrains, and implementations in bio-inspired robotics in turn offers insights to biological form and function. In particular, annelids (segmented worms), such asNereissp. (bristleworms), are useful subjects for their multi-modal locomotion through differing environments. This research aims to mimic key anatomical features of nereid worms in order to develop a new bio-inspired soft robot, named ‘Polysectoid’, that effectively moves through challenging terrains using both peristalsis and undulation. The muscles of the tendon-driven soft robots are longitudinal, and the robot has protruding structures mimicking parapodia and chaetae. Taking advantage of these features for both undulation and peristalsis required a new structural design to achieve both large bending motion and large diameter changes. Thus, the robot’s body is constructed of many long strips of flexible polymer, connected with custom 3D-printed channel pieces. We compare effectiveness and efficiency of movements of the resulting robot on substrates with different textures and in confined spaces. Parapodia and chaetae improve robot performance, with different effects on different gaits and substrates. Peristalsis with long parapodia allows Polysectoid to stay on a straightforward trajectory even without steering control. On the other hand, undulation allows the robot to navigate well in tight spaces, such as sandwiched between parallel surfaces, even when the distance between the parallel substrates was reduced to 66% of the robot’s diameter. This type of undulatory motion could have novel applications in inspections of confined spaces. As a detailed physical model, this design provides a platform to further examine the biomechanics of annelid-inspired locomotion and cascading neural pattern generator-based networks. 

The passive compliance of a soft worm-like body can be a key advantage for traversal of complex confined spaces, but in practice, the body’s stiffness and contact friction often require experimental adjustments. Here, for the first time, we develop a dynamic, 3D simulation that enables systematic testing of robot parameters (e.g. stiffness and friction) in different radius of curvature environments, which will help us better understand design trade-offs in creating soft robots that mimic worm-like locomotion. Specifically, we use the open-source physics engine MuJoCo because it is established for both biomechanical and robotic modeling, as well as multi-point contact dynamics, which are present in confined spaces. The model has sensory capabilities analogous to the stretch and tactile proprioception of an earthworm and is amenable to both feedforward and feedback control. After validating our model by comparing to our previous physical robot, we quantify locomotion performance over a range of friction coefficients, structural stiffnesses, and turning radii. We found that speed increased with friction coefficient on flat ground for higher stiffness models, but decreased with friction coefficient for lower stiffness models, both on flat ground and in pipe bends. For turning radii greater than 0.45 m, speed and stiffness also had a positive correlation, however, below the critical turning radius of 0.45 m, increasing stiffness had no appreciable influence on speed. This simulation can potentially be used to optimize designs for particular environments, to better understand the influence of passive vs. active control on individual and coupled segments, and perhaps offer a deeper understanding of how animals and robots can employ soft structures. For example, we can posit from our results that changing stiffness will not increase speed below the critical turning radius, meaning further experiments should focus on other parameters or actively controlled turning to improve speed through tighter turns. 

Stable Heteroclinic Channels (SHCs) are dynamical systems composed of connected saddle equilibria. This work demonstrates a control system that combines SHCs with movement primitives to enable swimming in a simulated six segment snake robot. We identify control system parameters for lateral undulation, where all joints oscillate with the same amplitude, and anguilliform swimming, where joint amplitudes increase linearly from the head to the tail. Swimming speed is improved by learning SHC movement primitive parameters. We also propose a method for adapting the gait amplitude and frequency with tactile sensor input to accommodate obstacles. Then, we evaluate the relationship between SHC movement primitive parameters and the resulting trajectories. The swimming speed and efficiency of SHC controllers for each gait are compared against a conventional serpenoid controller, which derives joint trajectories from sinusoids. Controllers are evaluated first in an unobstructed environment, then in straight passages of various widths, and finally in 65 randomly generated uneven channels. We find that the amplitudes of joint oscillations scale proportionally with the SHC controller parameters. Due to gait optimization, as well as adaptive amplitude and frequency in response to tactile input, the learned SHC control system exhibits an average 28.8% greater speed than a serpenoid controller that only adapts amplitude during contact. This research demonstrates that SHCs benefit from intuitive tuning like serpenoid control, while also effectively incorporating sensory information to generate smooth kinematic trajectories. 

Bio-inspired robot controllers are becoming more complex as we strive to make them more robust to, and flexible in, noisy, real-world environments. A stable heteroclinic network (SHN) is a dynamical system that produces cyclical state transitions using noisy input. SHN-based robot controllers enable sensory input to be integrated at the phase-space level of the controller, thus simplifying sensor-integrated, robot control methods. In this work, we investigate the mechanism that drives branching state trajectories in SHNs. We liken the branching state trajectories to decision-splits imposed into the system, which opens the door for more sophisticated controls–all driven by sensory input. This work provides guidelines to systematically define an SHN topology, and increase the rate at which desired decision states in the topology are chosen. Ultimately, we are able to control the rate at which desired decision states activate for input signal-to-noise ratios across six orders of magnitude. 

This paper details the development and validation of a dynamic 3D compliant worm-like robot model controlled by a Synthetic Nervous System (SNS). The model was built and simulated in the physics engine Mujoco which is able to approximate soft bodied dynamics and generate contact, gravitational, frictional, and internal forces. These capabilities allow the model to realistically simulate the movements and dynamic behavior of a physical soft-bodied worm-robot. For validation, the results of this simulation were compared to data gathered from a physical worm robot and found to closely match key behaviors such as deformation propagation along the compliant structure and actuator efficiency losses in the middle segments. The SNS controller was previously developed for a simple 2D kinematic model and has been successfully implemented on this 3D model with little alteration. It uses coupled oscillators to generate coordinated actuator control signals and induce peristaltic locomotion. This model will be useful for analyzing dynamic effects during peristaltic locomotion like contact forces and slip as well as developing and improving control algorithms that avoid unwanted slip. 

Creating burrows through natural soils and sediments is a problem that evolution has solved numerous times, yet burrowing locomotion is challenging for biomimetic robots. As for every type of locomotion, forward thrust must overcome resistance forces. In burrowing, these forces will depend on the sediment mechanical properties that can vary with grain size and packing density, water saturation, organic matter and depth. The burrower typically cannot change these environmental properties, but can employ common strategies to move through a range of sediments. Here we propose four challenges for burrowers to solve. First, the burrower has to create space in a solid substrate, overcoming resistance by e.g., excavation, fracture, compression, or fluidization. Second, the burrower needs to locomote into the confined space . A compliant body helps fit into the possibly irregular space, but reaching the new space requires non-rigid kinematics such as longitudinal extension through peristalsis, unbending, or eversion. Third, to generate the required thrust to overcome resistance, the burrower needs to anchor within the burrow . Anchoring can be achieved through anisotropic friction or radial expansion, or both. Fourth, the burrower must sense and navigate to adapt the burrow shape to avoid or access different parts of the environment. Our hope is that by breaking the complexity of burrowing into these component challenges, engineers will be better able to learn from biology, since animal performance tends to exceed that of their robotic counterparts. Since body size strongly affects space creation, scaling may be a limiting factor for burrowing robotics, which are typically built at larger scales. Small robots are becoming increasingly feasible, and larger robots with non-biologically-inspired anteriors (or that traverse pre-existing tunnels) can benefit from a deeper understanding of the breadth of biological solutions in current literature and to be explored by continued research. 

Shape‐morphing capabilities of metamaterials can be expanded by developing approaches that enable the integration of different types of cellular structures. Herein, a rational material design process is presented that fits together auxetic (anti‐tetrachiral) and non‐auxetic (the novel nodal honeycomb) lattice structures with a shared grid of nodes to obtain desired values of Poisson's ratios and Young's moduli. Through this scheme, deformation properties can be easily set piece by piece and 3D printed in useful combinations. For example, such nodally integrated tubular lattice structures undergo worm‐like peristalsis or snake‐like undulations that result in faster speeds than the monophasic counterpart in narrow channels and in wider channels, respectively. In a certain scenario, the worm‐like hybrid metamaterial structure traverses between confined spaces that are otherwise impassable for the isotropic variant. These deformation mechanisms allow us to design shape‐morphing structures into customizable soft robot skins that have improved performance in confined spaces. The presented analytical material design approach can make metamaterials more accessible for applications not only in soft robotics but also in medical devices or consumer products. 

Abstract Soft earthworm‐like robots that exhibit mechanical compliance can, in principle, navigate through uneven terrains and constricted spaces that are inaccessible to traditional legged and wheeled robots. However, unlike the biological originals that they mimic, most of the worm‐like robots reported to date contain rigid components that limit their compliance, such as electromotors or pressure‐driven actuation systems. Here, a mechanically compliant worm‐like robot with a fully modular body that is based on soft polymers is reported. The robot is composed of strategically assembled, electrothermally activated polymer bilayer actuators, which are based on a semicrystalline polyurethane with an exceptionally large nonlinear thermal expansion coefficient. The segments are designed on the basis of a modified Timoshenko model, and finite element analysis simulation is used to describe their performance. Upon electrical activation of the segments with basic waveform patterns, the robot can move through repeatable peristaltic locomotion on exceptionally slippery or sticky surfaces and it can be oriented in any direction. The soft body enables the robot to wriggle through openings and tunnels that are much smaller than its cross‐section.