Copebot: Underwater soft robot with copepod-like locomotion

It has been a great challenge to develop robots that are able to perform complex movement patterns with high speed and, simultaneously, high accuracy. Copepods are animals found in freshwater and saltwater habitats that can have extremely fast escape responses when a predator is sensed by performing explosive curved jumps. Here, we present a design and build prototypes of a combustion-driven underwater soft robot, the "copebot", that, like copepods, is able to accurately reach nearby predefined locations in space within a single curved jump. Because of an improved thrust force transmission unit, causing a large initial acceleration peak (850 Bodylength*s-2), the copebot is 8 times faster than previous combustion-driven underwater soft robots, whilst able to perform a complete 360{\deg} rotation during the jump. Thrusts generated by the copebot are tested to quantitatively determine the actuation performance, and parametric studies are conducted to investigate the sensitivities of the input parameters to the kinematic performance of the copebot. We demonstrate the utility of our design by building a prototype that rapidly jumps out of the water, accurately lands on its feet on a small platform, wirelessly transmits data, and jumps back into the water. Our copebot design opens the way toward high-performance biomimetic robots for multifunctional applications.Comment: 13 pages, 8 figures, research article. Soft Robotics, 202

Proxy-based sliding-mode tracking control of dielectric elastomer actuators through eliminating rate-dependent viscoelasticity

This work was partially supported by the State Key Laboratory of Mechanical Transmissions (SKLMT-ZDKFKT-202004) and the National Natural Science Foundation of China (52005322 and 52025057).Peer reviewedPostprin

Adhesion State Estimation for Electrostatic Gripper Based on Online Capacitance Measure

Electroadhesion is a suitable technology for developing grippers for applications where fragile, compliant or variable shape objects need to be grabbed and where a retention action is typically preferred to a compression force. This article presents a self-sensing technique for electroadhesive devices (EAD) based on the capacitance measure. Specifically, we demonstrate that measuring the variation of the capacitance between electrodes of an EAD during the adhesion can provide useful information to automatically detect the successful grip of an object and the possible loss of adhesion during manipulation. To this aim, a dedicated electronic circuit is developed that is able to measure capacitance variations while the high voltage required for the adhesion is activated. A test bench characterization is presented to evaluate the self-sensing of capacitance during different states: (1) the EAD is far away from the object to be grasped; (2) the EAD is in contact with the object, but the voltage is not active (i.e., no adhesion); and (3) the EAD is activated and attached to the object. Correlation between the applied voltage, object material and shape and capacitance is made. The self-sensing EAD is then demonstrated in a closed-loop robotic application that employs a robot manipulator arm to pick and place objects of different kinds

Stress tensor mesostructures for freeform shaping of thin substrates

Stress-induced shaping, which deforms thin substrates utilizing stressed surface coatings, has enabled and enhanced a host of applications in past decades. Owing to the touchless fabrication process compatible with modern planar technology, the method has been applied from microscale to macroscale applications such as self-assembled micro-structures and space mirrors. However, the deformations created by existing stress-control schemes are limited to certain classes of geometries (such as sphere, coma and astigmatism) or rely on boundary constraints and hinges because the stress is unary, e.g., equibiaxial stress or uniaxial stress with fixed orientation. Here, we present novel stress tensor mesostructures to spatially control the three required stress tensor components, i.e., two normal stresses and a shear stress, over the surface of thin substrates. Three different mesostructure types have been created, each offering distinct advantages. For demonstration, we patterned these mesostructures on the back sides of silicon wafers for freeform shape generation and correction which are not achievable by conventional methods. Stress tensor mesostructures will unleash the value of fields related to stress-induced bending from microscale to macroscopy, such as thin freeform substrates that will become increasingly important with the rise of wearable and space optics