Abstract
Many soft robots are made of polymeric materials such as silicone, urethane, and gel. These soft matter-based robots can be designed to have soft functionality by controlling the deformation, elastic modulus, and other properties of the flexible material. However, updating the shape of a soft robot once it is developed is not considered to be highly effective. It has to be redesigned whenever different functions are required. The body of a modular robot is composed of connected voxel units, and the model and behavior of the robot can be altered conveniently by recombining the module groups. This study proposes a reconfigurable and vacuum-actuated soft matter modular block: MORI-A. It can realize different behaviors of uniaxial bending, shear deformation, and non-deformation depending on the 3D-printed structure contained in a unit. In addition, MORI-A can display elastic anisotropy that depends on the density of the 3D structure it contains and the trajectory it adopts during fabrication. This study quantitatively verifies the load capacity and bendability of MORI-A. We then show that the combination of deformable shapes can improve the diversity of soft modular robot designs by simultaneously developing many curved deformations. These designs include characters. soft modular grippers, applications for underwater swimming robots using curvature, and artificial muscles for dolls made of cloth or cotton that are not supposed to move spontaneously.
Highlights
M ODULARITY in robotics can provide the reconfigurable functions and forms required for a robot to perform the tasks desired by the robotic designer [1]–[5]
By using soft materials for all the components including connectors, we can significantly reduce the deformation caused by the attachment of hard metals such as magnets, which is a challenge for conventional soft modular robots
This study proposed a soft robot module called ”MORIA.”
Summary
M ODULARITY in robotics can provide the reconfigurable functions and forms required for a robot to perform the tasks desired by the robotic designer [1]–[5]. Modularity has been observed in neural networks such as the animal brain, as well as in artificially constructed networks such as vascular networks, gene regulatory networks, protein– protein interaction networks, metabolic networks, and social networking services. That locally aggregated functionalities and topologies determine the properties of the entire system is an important concept. Modular robots are based on two approaches: the first is to integrate electronic devices such as micro-electromechanical systems into the robot to Manuscript received: October, 11, 2021; Revised December, 21, 2021; Accepted January, 4, 2022. This letter was recommended for publication by Editor Editor C.
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