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Soft robots are inherently compliant and have a strong potential to realize human-friendly and safe robots. Despite continued research highlighting the potential of soft robots, they remain largely confined to laboratory settings. In this work, inspired by spider monkeys' tails, we propose a hybrid soft robot (HSR) design. We detail the design objectives and methodology to improve the controllable stiffness range and achieve independent stiffness and shape control. We extend the curve parametric approach to obtain a kinematic model of the proposed HSR. We experimentally demonstrate that the proposed HSR has about 100% stiffness range increase than a previous soft robot design with identical physical dimensions. In addition, we empirically map HSR's bending shape-pressure-stiffness and present an application example-a soft robotic gripper-to demonstrate the decoupled nature of stiffness and shape variations. Experimental results show that proposed HSR can be successfully

Snakes are a remarkable evolutionary success story. Numerous snake-inspired robots have been proposed over the years. Soft robotic snakes (SRS), with their continuous and smooth bending capability, can better mimic their biological counterparts' unique characteristics. Prior SRSs are limited to planar operation with a limited number of planar gaits. We propose a novel SRS with spatial bending ability and investigate snake locomotion gaits beyond the planar gaits of the state-of-the-art systems. We derive a complete floating-base kinematic model of the SRS and use the model to derive joint-space trajectories for serpentine and inward/outward rolling locomotion gaits. These gaits are experimentally validated under varying frequency and amplitude of gait cycles. The results qualitatively and quantitatively validate the proposed SRSs' ability to leverage spatial bending to achieve locomotion gaits not possible with current SRS designs.

Compliant grasping is crucial for secure handling objects not only vary in shapes but also in mechanical properties. We propose a novel soft robotic gripper with decoupled stiffness and shape control capability for performing adaptive grasping with minimum system complexity. The proposed soft fingers conform to object shapes facilitating the handling of objects of different types, shapes, and sizes. Each soft gripper finger has a length constraining mechanism (an articulable rigid backbone) and is powered by pneumatic muscle actuators. We derive the kinematic model of the gripper and use an empirical approach to simultaneously map input pressures to stiffness control and bending deformation of fingers. We use these mappings to demonstrate decoupled stiffness and shape (bending) control of various grasping configurations. We conduct tests to quantify the grip quality when holding objects as the gripper changes orientation, the ability to maintain the grip as the gripper is subjected to translational and rotational movements, and the external force perturbations required to release the object from the gripper under various stiffness and shape (bending) settings. The results validate the proposed gripper's performance and show how the decoupled stiffness and shape control can improve the grasping quality in soft robotic grippers.

Numerous soft and continuum robotic manipulators have demonstrated their potential for compliant operation in highly unstructured environments or near people. Despite their recent popularity, modeling of their smooth bending deformation remains a challenge. For soft continuum manipulators, the widespread, constant curvature approach to modeling is inadequate for modeling some deformations that occur in practice, such as combined bending and twisting deformations. In this paper, we extend the classical Cosserat rod approach to model a variable-length, pneumatic soft continuum arm. We model the deformation of a pneumatically driven soft continuum manipulator, and the model is then compared against experimental data collected from a three degree of freedom, pneumatically actuated, soft continuum manipulator. The model shows good agreement in capturing the overall behavior of the bending deformation, with mean Euclidean error at the tip of the robot of 2.48 cm for a 22 cm long robot. In addition, the model shows good numerical stability for simulating long duration computations.

Multisection continuum arms offer complementary characteristics to those of traditional rigid-bodied robots. Inspired by biological appendages, such as elephant trunks and octopus arms, these robots trade rigidity for compliance and accuracy for safety and, therefore, exhibit strong potential for applications in human-occupied spaces. Prior work has demonstrated their superiority in operation in congested spaces and manipulation of irregularly shaped objects. However, they are yet to be widely applied outside laboratory spaces. One key reason is that, due to compliance, they are difficult to control. Sophisticated and numerically efficient dynamic models are a necessity to implement dynamic control. In this paper, we propose a novel numerically stable center-of-gravity-based dynamic model for variable-length multisection continuum arms. The model can accommodate continuum robots having any number of sections with varying physical dimensions. The dynamic algorithm is of O(n2) complexity, runs at 9.5 kHz, simulates six to eight times faster than real time for a three-section continuum robot, and, therefore, is ideally suited for real-time control implementations. The model accuracy is validated numerically against an integral-dynamic model proposed by the authors and experimentally for a three-section pneumatically actuated variable-length multisection continuum arm. This is the first sub-real-time dynamic model based on a smooth continuousmore »deformation model for variable-length multisection continuum arms.« less

Soft Continuum arms, such as trunk and tentacle robots, can be considered as the “dual” of traditional rigid-bodied robots in terms of manipulability, degrees of freedom, and compliance. Introduced two decades ago, continuum arms have not yet realized their full potential, and largely remain as laboratory curiosities. The reasons for this lag rest upon their inherent physical features such as high compliance which contribute to their complex control problems that no research has yet managed to surmount. Recently, reservoir computing has been suggested as a way to employ the body dynamics as a computational resource toward implementing compliant body control. In this paper, as a first step, we investigate the information processing capability of soft continuum arms. We apply input signals of varying amplitude and bandwidth to a soft continuum arm and generate the dynamic response for a large number of trials. These data is aggregated and used to train the readout weights to implement a reservoir computing scheme. Results demonstrate that the information processing capability varies across input signal bandwidth and amplitude. These preliminary results demonstrate that soft continuum arms have optimal bandwidth and amplitude where one can implement reservoir computing.

We study the path planning problem for continuum-arm robots, in which we are given a starting and an end point, and we need to compute a path for the tip of the continuum arm between the two points. We consider both cases where obstacles are present and where they are not. We demonstrate how to leverage the continuum arm features to introduce a new model that enables a path planning approach based on the configurations graph, for a continuum arm consisting of three sections, each consisting of three muscle actuators. The algorithm we apply to the configurations graph allows us to exploit parallelism in the computation to obtain efficient implementation. We conducted extensive tests, and the obtained results show the completeness of the proposed algorithm under the considered discretizations, in both cases where obstacles are present and where they are not. We compared our approach to the standard inverse kinematics approach. While the inverse kinematics approach is much faster when successful, our algorithm always succeeds in finding a path or reporting that no path exists, compared to a roughly 70% success rate of the inverse kinematics approach (when a path exists).

In this paper, we propose and investigate a new approach to modeling variable curvature continuum robot sections, based on Euler spirals. Euler spirals, also termed Clothoids, or Cornu spirals, are those curves in which the curvature increases linearly with their arc length. In this work, Euler spirals are applied to the kinematic modeling of continuum robots for the first time. The approach was evaluated using the sections of numerous continuum robots, including two novel parallel continuum robots. Each robot consists of three parallel sections, each with three thin, long McKibben actuators. These sections are poorly modeled by the widely used constant curvature kinematic model. The constant curvature and Euler spiral models were compared and the Euler spiral method was seen to be a significantly better match for a wide range of configurations of the robot hardware.