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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. 

Continuum robots have strong potential for application in Space environments. However, their modeling is challenging in comparison with traditional rigid-link robots. The Kinematic-Model-Free (KMF) robot control method has been shown to be extremely effective in permitting a rigid-link robot to learn approximations of local kinematics and dynamics (“kinodynamics”) at various points in the robot’s task space. These approximations enable the robot to follow various trajectories and even adapt to changes in the robot’s kinematic structure. In this paper, we present the adaptation of the KMF method to a three-section, nine degrees-of-freedom continuum manipulator for both planar and spatial task spaces. Using only an external 3D camera, we show that the KMF method allows the continuum robot to converge to various desired set points in the robot’s task space, avoiding the complexities inherent in solving this problem using traditional inverse kinematics. The success of the method shows that a continuum robot can “learn” enough information from an external camera to reach and track desired points and trajectories, without needing knowledge of exact shape or position of the robot. We similarly apply the method in a simulated example of a continuum robot performing an inspection task on board the ISS. 

The discipline called biomimetics attempts to create synthetic systems that model the behavior and functions of biological systems. At a very basic level, this approach incorporates a philosophy grounded in modeling either the behavior or properties of organic systems based on inferences of structure–function relationships. This approach has achieved extraordinary scientific accomplishments, both in fabricating new materials and structures. However, it is also prone to misstep because (1) many organic structures are multifunctional that have reconciled conflicting individual functional demands (rather than maximize the performance of any one task) over evolutionary time, and (2) some structures are ancillary or entirely superfluous to the functions their associated systems perform. The important point is that we must typically infer function from structure, and that is not always easy to do even when behavioral characteristics are available (e.g., the delivery of venom by the fangs of a snake, or cytoplasmic toxins by the leaf hairs of the stinging nettle). Here, we discuss both of these potential pitfalls by comparing and contrasting how engineered and organic systems are operationally analyzed. We also address the challenges that emerge when an organic system is modeled and suggest a few methods to evaluate the validity of models in general. 

Continuum robots have long held a great potential for applications in inspection of remote, hard-to-reach environments. In future environments such as the Deep Space Gateway, remote deployment of robotic solutions will require a high level of autonomy due to communication delays and unavailability of human crews. In this work, we explore the application of policy optimization methods through Actor-Critic gradient descent in order to optimize a continuum manipulator’s search method for an unknown object. We show that we can deploy a continuum robot without prior knowledge of a goal object location and converge to a policy that finds the goal and can be reused in future deployments. We also show that the method can be quickly extended for multiple Degrees-of-Freedom and that we can restrict the policy with virtual and physical obstacles. These two scenarios are highlighted using a simulation environment with 15 and 135 unique states, respectively. 

In this paper, a novel strategy is designed for trajectory control of a multi-section continuum robot in three-dimensional space to achieve accurate orientation, curvature, and section length tracking. The formulation connects the continuum manipulator dynamic behavior to a virtual discrete-jointed robot whose degrees-of-freedom are directly mapped to those of a continuum robot section. Based on this connection, a computed torque control architecture is developed for the virtual robot, for which inverse kinematics and dynamic equations are constructed and exploited, with appropriate transformations developed for implementation on the continuum robot. The control algorithm is implemented on a six degree-of-freedom two-section OctArm continuum manipulator. Experimental results show that the proposed method managed simultaneous extension/contraction, bending, and torsion actions on multi-section continuum robots with decent tracking performance (steady state arc length and curvature tracking error of merely 3.3mm and 0.13m-1, respectively). These results demonstrate that the proposed method can be applied to multi-section continuum manipulator and perform complex maneuvers within a nonlinear setting. 

Tendon actuated multisection continuum arms have high potential for inspection applications in highly constrained spaces. They generate motion by axial and bending deformations. However, because of the high mechanical coupling between continuum sections, variable length-based kinematic models produce poor results. A new mechanics model for tendon actuated multisection continuum arms is proposed in this paper. The model combines the continuum arm curve parameter kinematics and concentric tube kinematics to correctly account for the large axial and bending deformations observed in the robot. Also, the model is computationally efficient and utilizes tendon tensions as the joint space variables thus eliminating the actuator length related problems such as slack and backlash. A recursive generalization of the model is also presented. Despite the high coupling between continuum sections, numerical results show that the model can be used for generating correct forward and inverse kinematic results. The model is then tested on a thin and long multisection continuum arm. The results show that the model can be used to successfully model the deformation. 

We describe a novel haptic interface designed specifically for the teleoperation of extensible continuum manipulators. The proposed device is based off of, and extends to the haptic domain, a kinematically similar input device for continuum manipulators called the MiniOct. This letter describes the physical design of the new device, the method of creating impedance-type haptic feedback to users, and some of the requirements for implementing this device in a bilateral teleoperation scheme for continuum robots. We report a series of initial experiments to validate the operation of the system, including simulated and real-time conditions. The experimental results show that a user can identify the direction of planar obstacles from the feedback for both virtual and physical environments. Finally, we discuss the challenges for providing feedback to an operator about the state of a teleoperated continuum manipulator. 

We discuss a novel variable topology continuum robot designed and constructed under NASA-funded research. The robot features a seven degree of freedom continuous backbone “trunk”, with two pairs of “branches”: two “tendril” effectors and two support “roots”. Each of the pairs of branches can be fully retracted inside the trunk, allowing it to penetrate congested environments as a single slender unit, and subsequently deploy the branches to perform a variety of tasks. The “roots” provide physical support, while the two effectors and trunk tip enable independent but coordinated functionality: sensing (vision) in one tendril, and manipulation at two scales, via the second tendril and the trunk tip. The specifics of the new design are described and discussed in detail. We illustrate the operation and potential applications of the new design via a series of demonstrations, particularly cleaning of dust from solar panels.