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 lowcost 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 graspingUnited States. Defense Advanced Research Projects Agency (award number FA8650-15-C-7548)National Science Foundation (U.S.) (award number 1830901)Wyss Institute for Biologically Inspired EngineeringJD.co

A Thermoplastic Elastomer Belt Based Robotic Gripper

Novel robotic grippers have captured increasing interests recently because of their abilities to adapt to varieties of circumstances and their powerful functionalities. Differing from traditional gripper with mechanical components-made fingers, novel robotic grippers are typically made of novel structures and materials, using a novel manufacturing process. In this paper, a novel robotic gripper with external frame and internal thermoplastic elastomer belt-made net is proposed. The gripper grasps objects using the friction between the net and objects. It has the ability of adaptive gripping through flexible contact surface. Stress simulation has been used to explore the regularity between the normal stress on the net and the deformation of the net. Experiments are conducted on a variety of objects to measure the force needed to reliably grip and hold the object. Test results show that the gripper can successfully grip objects with varying shape, dimensions, and textures. It is promising that the gripper can be used for grasping fragile objects in the industry or out in the field, and also grasping the marine organisms without hurting them

A Lightweight Universal Gripper with Low Activation Force for Aerial Grasping

Soft robotic grippers have numerous advantages that address challenges in dynamic aerial grasping. Typical multi-fingered soft grippers recently showcased for aerial grasping are highly dependent on the direction of the target object for successful grasping. This study pushes the boundaries of dynamic aerial grasping by developing an omnidirectional system for autonomous aerial manipulation. In particular, the paper investigates the design, fabrication, and experimental verification of a novel, highly integrated, modular, sensor-rich, universal jamming gripper specifically designed for aerial applications. Leveraging recent developments in particle jamming and soft granular materials, the presented gripper produces a substantial holding force while being very lightweight, energy-efficient and only requiring a low activation force. We show that the holding force can be improved by up to 50% by adding an additive to the membrane's silicone mixture. The experiments show that our lightweight gripper can develop up to 15N of holding force with an activation force as low as 2.5N, even without geometric interlocking. Finally, a pick and release task is performed under real-world conditions by mounting the gripper onto a multi-copter. The developed aerial grasping system features many useful properties, such as resilience and robustness to collisions and the inherent passive compliance which decouples the UAV from the environment.Comment: 21 pages, 19 figures; corrected affiliation

Directly 3D Printed, Pneumatically Actuated Multi-Material Robotic Hand

Soft robotic manipulators with many degrees of freedom can carry out complex tasks safely around humans. However, manufacturing of soft robotic hands with several degrees of freedom requires a complex multi-step manual process, which significantly increases their cost. We present a design of a multi-material 15 DoF robotic hand with five fingers including an opposable thumb. Our design has 15 pneumatic actuators based on a series of hollow chambers that are driven by an external pressure system. The thumb utilizes rigid joints and the palm features internal rigid structure and soft skin. The design can be directly 3D printed using a multi-material additive manufacturing process without any assembly process and therefore our hand can be manufactured for less than 300 dollars. We test the hand in conjunction with a low-cost vision-based teleoperation system on different tasks.Comment: 7 pages, 16 figure

A Lightweight Universal Gripper with Low Activation Force for Aerial Grasping

Soft robotic grippers have numerous advantages that address challenges in dynamic aerial grasping. Typical multi-fingered soft grippers recently showcased for aerial grasping are highly dependent on the direction of the target object for successful grasping. This study pushes the boundaries of dynamic aerial grasping by developing an omnidirectional system for autonomous aerial manipulation. In particular, the paper investigates the design, fabrication, and experimental verification of a novel, highly integrated, modular, sensor-rich, universal jamming gripper specifically designed for aerial applications. Leveraging recent developments in particle jamming and soft granular materials, the presented gripper produces a substantial holding force while being very lightweight, energy-efficient and only requiring a low activation force. We show that the holding force can be improved by up to 50% by adding an additive to the membrane’s silicone mixture. The experiments show that our lightweight gripper can develop up to 15N of holding force with an activation force as low as 2.5N, even without geometric interlocking. Finally, a pick and release task is performed under real-world conditions by mounting the gripper onto a multi-copter. The developed aerial grasping system features many useful properties, such as resilience and robustness to collisions and the inherent passive compliance which decouples the UAV from the environment

Design of 3D-Printed Soft Pneunet Actuator for Robotic Fruit Harvesting

This work details the development of 3D printed TPU pneunet actuators for use in apple picking for agricultural soft robotics. These actuators increase the force capabilities of soft pneunets and simplify manufacturing. Grippers based on these actuators are ideal for apple picking because they are compliant, yet exert sufficient force to pull ripe apples from branches. The pneunets are printed in Cheetah TPU with an interior chamber size to wall thickness ratio of 2 mm to 1 mm, respectively. The actuators were optimized for a maximum pull force of 23 N which, when combined in a three-finger grip, can provide up to 69 N of pulling force. The block force test revealed a maximum force output of 22.77 N ± 1.41. The radius of curvature test revealed a minimum radius of curvature of 22.41 with the medium finger at a pressure of 30 psi which can grasp apples from 65.4 mm to 75.0 mm in diameter. The actuators can be combined into a gripper with a minimum of 3 actuators. This type of gripper could enable apple harvesting while maintaining compliance for protecting the fruit from bruising. Future work includes assembling the pneunets into a three-actuator gripper.Key Words: Pneunet, TPU, Actuator, Soft Robotics, Three Dimensional (3D) Printin

A Programmably Compliant Origami Mechanism for Dynamically Dexterous Robots

We present an approach to overcoming challenges in dynamical dexterity for robots through programmably compliant origami mechanisms. Our work leverages a one-parameter family of flat sheet crease patterns that folds into origami bellows, whose axial compliance can be tuned to select desired stiffness. Concentrically arranged cylinder pairs reliably manifest additive stiffness, extending the programmable range by nearly an order of magnitude and achieving bulk axial stiffness spanning 200–1500 N/m using 8 mil thick polyester-coated paper. Accordingly, we design origami energy-storing springs with a stiffness of 1035 N/m each and incorporate them into a three degree-of-freedom (DOF) tendon-driven spatial pointing mechanism that exhibits trajectory tracking accuracy less than 15% rms error within a (2 cm)^3 volume. The origami springs can sustain high power throughput, enabling the robot to achieve asymptotically stable juggling for both highly elastic (1 kg resilient shotput ball) and highly damped (“medicine ball”) collisions in the vertical direction with apex heights approaching 10 cm. The results demonstrate that “soft” robotic mechanisms are able to perform a controlled, dynamically actuated task

Investigating pre-touch sensing to predict grip success in compliant grippers using machine learning techniques

This work explores the application of pre-touch sensing to a compliant gripper in order to navigate the last few centimeters while grasping fruit in an occluded, cluttered environment. Machine learning was used in conjunction with pre-touch sensors to provide qualitative feedback about the success of the gripper in picking the target fruit prior to contact. Three compliant grippers were each designed to pick a specific fruit (miracle berries, cherry tomatoes and small figs) without damaging them. These grippers were designed to be mounted on the hybrid soft-rigid arm of a mobile field robot. An IR reflectance, time of flight and color sensor were used as pre-touch sensors and arranged on the gripper in various combinations to explore the contribution of each sensor. The gripper-sensor system was trained by positioning it relative to a dummy fruit using a 6 DOF arm and gripping the target. Using the training data, five machine learning methods were explored: nearest neighbor, decision trees, support vector machines, multi-layer perceptrons and a naive Bayes classifier. The various sensor configuration-machine learning combinations were tested and evaluated based on their ability to predict grip success. Additional training was conducted to demonstrate the ability to differentiate fruit from foreign matter (e.g. leaves) that are in the gripper opening. Time of flight sensors using nearest neighbor and support vector machines along with the set of all three sensors using support vector machines and multi-layer perceptrons showed the highest prediction precision (= 90%) with the color sensor playing a key role in detecting foreign objects. The machine learning methods were similar in their ability to predict grip success with nearest neighbor showing the best overall results, while sensor ‘richness’ play an important role in differentiating the sensors with the three sensor combination showing the best results