Rigid–Soft Interactive Design of a Lobster-Inspired Finger Surface for Enhanced Grasping Underwater

Bio-inspirations from soft-bodied animals provide a rich design source for soft robots, yet limited literature explored the potential enhancement from rigid-bodied ones. This paper draws inspiration from the tooth profiles of the rigid claws of the Boston Lobster, aiming at an enhanced soft finger surface for underwater grasping using an iterative design process. The lobsters distinguish themselves from other marine animals with a pair of claws capable of dexterous object manipulation both on land and underwater. We proposed a 3-stage design iteration process that involves raw imitation, design parametric exploration, and bionic parametric exploitation on the original tooth profiles on the claws of the Boston Lobster. Eventually, 7 finger surface designs were generated and fabricated with soft silicone. We validated each design stage through many vision-based robotic grasping attempts against selected objects from the Evolved Grasping Analysis Dataset (EGAD). Over 14,000 grasp attempts were accumulated on land (71.4%) and underwater (28.6%), where we selected the optimal design through an on-land experiment and further tested its capability underwater. As a result, we observed an 18.2% improvement in grasping success rate at most from a resultant bionic finger surface design, compared with those without the surface, and a 10.4% improvement at most compared with the validation design from the previous literature. Results from this paper are relevant and consistent with the bioresearch earlier in 1911, showing the value of bionics. The results indicate the capability and competence of the optimal bionic finger surface design in an amphibious environment, which can contribute to future research in enhanced underwater grasping using soft robots.

Development of Topology Optimized Bending-Twisting Soft Finger

This paper proposed a systematic framework to automatically design and fabricate optimized soft robotic fingers. The soft finger is composed of a soft silicone structure with inner air chambers and a harder outer layer, which are fabricated by molding process and 3D printing, respectively. The softer layer is utilized for actuation while the supportive hard structure is used to impose constraints. The framework applies a topology optimization approach based on RAMP method to obtain an optimal design of the outer layer of the soft fingers. Two basic motion primitives (bending and twisting) of the soft finger were explored. A multi-segmented soft bending finger and a soft twisting finger were designed and fabricated through the proposed framework. This work also explored the combination of bending and twisting primitives by developing a combined bending-twisting soft finger. The soft fingers were characterized by free and blocked movement tests. The experiments showed that the triple-segmented soft finger can achieve a maximum of 50.5 no-load bending under the actuation pressure of 53 kPa. The blocked movement test on the multi-segmented soft actuating finger showed that this finger could generate up to a maximum of 0.63 N force under 57 kPa actuation pressure in 7 seconds of inflating time. The developed twisting soft finger was shown to achieve tip rotation of up to 219 degrees under 29 kPa actuation pressure. Finally, the potential capability of the bending-twisting soft fingers was verified through applications like screwing and object grasping.

Trigger-Based Dexterous Operation with Multimodal Sensors for Soft Robotic Hand

This paper focuses on how to improve the operation ability of a soft robotic hand (SRH). A trigger-based dexterous operation (TDO) strategy with multimodal sensors is proposed to perform autonomous choice operations. The multimodal sensors include optical-based fiber curvature sensor (OFCS), gas pressure sensor (GPS), capacitive pressure contact sensor (CPCS), and resistance pressure contact sensor (RPCS). The OFCS embedded in the soft finger and the GPS series connected in the gas channel are used to detect the curvature of the finger. The CPCS attached on the fingertip and the RPCS attached on the palm are employed to detect the touch force. The framework of TDO is divided into sensor detection and action operation. Hardware layer, information acquisition layer, and decision layer form the sensor detection module; action selection layer, actuator drive layer, and hardware layer constitute the action operation module. An autonomous choice decision unit is used to connect the sensor detecting module and action operation module. The experiment results reveal that the TDO algorithm is effective and feasible, and the actions of grasping plastic framework, pinching roller ball pen and screwdriver, and handshake are executed exactly.

Arrangement of soft fingers for automatic grasping of fabric pieces of garment

It is a global challenge in the textile and apparel industry to grasp and separate fabric pieces automatically using mechanical devices. This paper summarized studies on grasping a textile cutting piece by different principles and mechanical systems, and focused on bionic soft fingers made of silica gel. In the study, we first tested single-point grasping to explore the factors that influence the grasping effects of soft fingers, and found that (a) the grasping margin is a crucial factor that influences the effect of grasping, (b) the sides and the directions of a piece play important roles in grasping, and a reverse side and a vertical direction often bring better results of grasping, and (c) although the opening distance of a soft finger is significant to the result of grasping, successful grasping is a joint result of the grasping margin and the opening distance. We then experimented with the arrangement of soft fingers, and discovered that (a) the shape and the area of a cut piece are the determinants for the number of soft fingers that have to be used, (b) a soft finger is needed at the intersections of a piece to guarantee unfolded grasping and transferring, and (c) the number of soft fingers to be used for a specific grasping task can be estimated after major factors are determined. The conclusion we proposed is easy to understand and is convenient for training or application in an industrial production. In the future, it is expected to be applied to the intelligent production of clothing.

Study on the Articulated Finger Based on Pneumatic Soft Joint

This article described a novel pneumatic soft joint used to make articulated soft fingers. This soft joint was designed by improving the basic structure of the fast pneumatic network. The joint was made of high modulus E630 silicon, which can increase the reverse exhaust speed through its high structural elasticity. Aramid fabric was used to restrain the non-working direction of joints to reduce ineffective expansion, thereby reducing air consumption. The kinematics and statics model of the joint was established by the piecewise constant curvature (PCC) method, and the model was proved to be effective. The silicone staging pouring process was used in the manufacture of joints and fingers, which can achieve high-quality product rates. A soft finger actuator composed of three soft joints was designed and manufactured, whose length was 1.3 times the human finger. The finger can nimbly achieve the target motion, and the gripping force of the fingertip can reach 7.1N. The articulated soft finger actuator has applications in soft dextrous hands and soft gripper.

Planning to Flip Heavy Objects Considering Soft-Finger Contacts

In this study, we implemented a constrained motion planner that enables robot manipulators to flip large and heavy objects without slippage while continuously holding them. Based on the soft-finger maximum friction torque, we developed a constraint relaxation method to estimate the critical rotation angle that a robot end effector can rotate while avoiding in-hand slippage. The critical rotation angle was used in a motion planner to sample safe configurations and generate slippage-free motion. The proposed planner was implemented using a 6-degree-of-freedom robot arm and a 2-finger robotic gripper with rubber pads attached to the fingertips. Experiments were performed with several objects to examine and demonstrate the performance of the planner. The results indicated satisfying planning time and the elimination of object slippage.