Lattice-and-Plate Model: Mechanics Modeling of Physical Origami Robots

Programmable Shape-Shifting Soft Robotic Structure Using Liquid Metal Electromagnetic Actuators

Constant development of soft robots, stretchable electronics, or flexible medical devices forces the research to look for new flexible structures that can change their shapes under external physical stimuli. This study presents a soft robotic structure that can change its shape into different three-dimensional (3D) configurations in response to electric current flown through the embedded liquid-metal conductors enabling electromagnetic actuation. The proposed structure is composed of volumetric pixels (voxels) connected in series where each can be independently controlled by the inputs of electrical current and vacuum pressure. A single voxel is made up of a granular core (GC) with an outer shell made of silicone rubber. The shell has embedded channels filled with liquid metal. The structure changes its shape under the Lorentz force produced by the liquid metal channel under applied electrical current. The GC allows the structure to maintain its shape after deformation even when the current is shut off. This is possible due to the granular jamming effect. In this study, we show the concept, the results of multiphysics simulation, and experimental characterization, including among other techniques, such as 3D digital image correlation or 3D magnetic field scanning, to study the different properties of the structure. We prove that the proposed structure can morph into many different shapes with the amplitude higher than 10 mm, and this process can be both fully reversible and repeatable.

Deep-Learning-Assisted Sensor with Multiple Perception Capabilities for an Intelligent Driver Assistance Monitoring System

Driver assistance systems can help drivers achieve better control of their vehicles while driving and reduce driver fatigue and errors. However, the current driver assistance devices have a complex structure and severely violate the privacy of drivers, hindering the development of driver assistance technology. To address these limitations, this article proposes an intelligent driver assistance monitoring system (IDAMS), which combines a Kresling origami structure-based triboelectric sensor (KOS-TS) and a convolutional neural network (CNN)-based data analysis. For different driving behaviors, the output signals of the KOS-TSs contain various features, such as a driver's pressing force, pressing time, and sensor triggering sequence. This study develops a multiscale CNN that employs different pooling methods to process KOS-TS data and analyze temporal information. The proposed IDAMS is verified by driver identification experiments, and the results show that the accuracy of the IDAMS in discriminating eight different users is improved from 96.25% to 99.38%. In addition, the results indicate that IDAMS can successfully monitor driving behaviors and can accurately distinguish between different driving behaviors. Finally, the proposed IDAMS has excellent hands-off detection (HOD), identification, and driving behavior monitoring capabilities and shows broad potential for application in the fields of safety warning, personalization, and human-computer interaction.