Abstract Soft robotic grippers can gently grasp and maneuver objects. However, they are difficult to model and control due to their highly deformable fingers and complex integration with robotic systems. This paper investigates the design requirements as well as the grasping capabilities and performance of a soft gripper system based on fluidic prestressed composite (FPC) fingers. An analytical model is constructed as follows: each finger is modeled using the chained composite model (CCM); strain energy and work done by pressure and loads are computed using polynomials with unknown coefficients; net energy is minimized using the Rayleigh–Ritz method to calculate the deflected equilibrium shapes of the finger as a function of pressure and loads; and coordinate transformation and gripper geometries are combined to analyze the grasping performance. The effects of prestrain, integration angle, and finger overlap on the grasping performance are examined through a parametric study. We also analyze gripping performance for cuboidal and spherical objects and show how the grasping force can be controlled by varying fluidic pressure. The quasi-static responses of fabricated actuators are measured under pressures and loads. It is shown that the actuators’ modeled responses agree with the experimental results. This work provides a framework for the theoretical analysis of soft robotic grippers and the methods presented can be extended to model grippers with different types of actuation.
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A Vacuum-driven Origami “Magic-ball” Soft Gripper
Soft robotics has yielded numerous examples of soft grippers that utilize compliance to achieve impressive grasping performances with great simplicity, adaptability, and robustness. Designing soft grippers with substantial grasping strength while remaining compliant and gentle is one of the most important challenges in this field. In this paper, we present a light-weight, vacuum-driven soft robotic gripper made of an origami “magic-ball” and a flexible thin membrane. We also describe the design and fabrication method to rapidly manufacture the gripper with different combinations of low- cost materials for diverse applications. Grasping experiments demonstrate that our gripper can lift a large variety of objects, including delicate foods, heavy bottles, and other miscellaneous items. The grasp force on 3D-printed objects is also characterized through mechanical load tests. The results reveal that our soft gripper can produce significant grasp force on various shapes using negative pneumatic pressure (vacuum). This new gripper holds the potential for many practical applications that require safe, strong, and simple grasping.
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- Award ID(s):
- 1830901
- NSF-PAR ID:
- 10110043
- Date Published:
- Journal Name:
- International Conference on Robotics and Automation (ICRA)
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract Achieving multicapability in a single soft gripper for handling ultrasoft, ultrathin, and ultraheavy objects is challenging due to the tradeoff between compliance, strength, and precision. Here, combining experiments, theory, and simulation, we report utilizing angle-programmed tendril-like grasping trajectories for an ultragentle yet ultrastrong and ultraprecise gripper. The single gripper can delicately grasp fragile liquids with minimal contact pressure (0.05 kPa), lift objects 16,000 times its own weight, and precisely grasp ultrathin, flexible objects like 4-μm-thick sheets and 2-μm-diameter microfibers on flat surfaces, all with a high success rate. Its scalable and material-independent design allows for biodegradable noninvasive grippers made from natural leaves. Explicitly controlled trajectories facilitate its integration with robotic arms and prostheses for challenging tasks, including picking grapes, opening zippers, folding clothes, and turning pages. This work showcases soft grippers excelling in extreme scenarios with potential applications in agriculture, food processing, prosthesis, biomedicine, minimally invasive surgeries, and deep-sea exploration.
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null (Ed.)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.more » « less
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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.more » « less
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This paper aims to improve robots’ versatility and adaptability by allowing them to use a large variety of end- effector tools and quickly adapt to new tools. We propose AdaGrasp, a method to learn a single grasping policy that generalizes to novel grippers. By training on a large collection of grippers, our algorithm is able to acquire generalizable knowledge of how different grippers should be used in various tasks. Given a visual observation of the scene and the gripper, AdaGrasp infers the possible grasp poses and their grasp scores by computing the cross convolution between the shape encodings of the gripper and scene. Intuitively, this cross convolution operation can be considered as an efficient way of exhaustively matching the scene geometry with gripper geometry under different grasp poses (i.e., translations and orientations), where a good "match" of 3D geometry will lead to a successful grasp. We validate our methods in both simulation and real- world environments. Our experiment shows that AdaGrasp significantly outperforms the existing multi-gripper grasping policy method, especially when handling cluttered environments and partial observations. Code and Data are available at https://adagrasp.cs.columbia.edu.more » « less
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Accepted Manuscript1.0
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