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Creating diverse microparticle patterns on a large scale enhances the performance and efficiency of biochemical analytics. Current techniques exhibit limitations in achieving diverse microparticle patterns on a large scale, primarily focusing on patterning particles of the same type with limited flexibility and accessibility. Moreover, accessibility to patterned particles without a fixed formation poses additional challenges. Herein, in this work, we introduce a novel robotic micropatterning system designed to address these challenges. The system facilitates the selection, batch transferring, patterning, and encapsulation of microparticles using negative dielectrophoresis (nDEP)-tweezers, enabling large-scale microparticle patterning on a hydrogel. A multielectrode chip was mounted on a micromanipulator to serve as the nDEP tweezers, and the microparticles scattering on the substrate could be trapped and displaced to different positions on a substrate with an array of holes for large-scale pattern generation. Photosensitive hydrogel was employed for microparticle pattern encapsulation. The effects of configuring different experimental parameters on the patterning efficiency were evaluated and analyzed. Experiments were conducted to explore the stability and performance of the micropatterns. Various patterns with hydrogel encapsulation were created using different color polystyrene microbeads (orange, blue, and green) with varying sizes (50, 100, and 125 μm) under the adjusted environment. Results demonstrate the successful creation of large-scale microbead patterns in a specified form and their encapsulation into an extractable hydrogel using the proposed nDEP tweezer system. The proposed system can be potentially applied to diverse bioparticles for analysis.

To steer a soft robot precisely in an unconstructed environment with minimal collision remains an open challenge for soft robots. When the environments are unknown, prior motion planning for navigation may not always be available. This article presents a novel Sim-to-Real method to guide a cable-driven soft robot in a static environment under the Simulation Open Framework Architecture (SOFA). The scenario aims to resemble one of the steps during a simplified transoral tracheal intubation process where a robotic endotracheal tube is guided to the upper trachea–larynx location by a flexible video-assisted endoscope/stylet. In SOFA, we employ the quadratic programming inverse solver to obtain collision-free motion strategies for the endoscope/stylet manipulation based on the robot model and encode the virtual eye-in-hand vision. Then, we associate the anatomical features recognized by the virtual vision and the joint space motion using a closed-loop nonlinear autoregressive exogenous model (NARX) network. Afterward, we transfer the learned knowledge to the robot prototype, expecting it to navigate to the desired spot in a new phantom environment automatically based on its eye-in-hand vision only. Experiment results indicate that our soft robot can efficaciously navigate through the unstructured phantom to the desired spot with minimal collision motion according to what it has learned from the virtual environment. The results show that the average R-squared coefficient between the closed-loop NARX-forecasted and SOFA-referenced robot's cable and prismatic joint space motion are 0.963 and 0.997, respectively. The eye-in-hand visions also demonstrate a good alignment between the robot tip and the glottis.

The hyper redundancy and flexibility of continuum robots have made them attractive to replace conventional rigid-link robots in many applications. Although the robot body can be bent to different directions as needed, rotation along the backbone is not a feature available in many continuum robot designs. In this article, we propose a pneumatic-driven, dual-segment continuum robot with the capability of providing continuous rotation along the deformed backbone (CRADB) at a particular point. A slip ring was employed to enable rapid coupling/decoupling between the air supply and different chambers of the robot, while a stepper motor with adjustable speed was adopted to provide the additional rotational motion. A novel control scheme based on optimization was developed to compute the required shape configuration under different modes. Control schemes toward fast deformation motion and CRADB motion have been, respectively, built. Experiments were conducted to compare the performance in positioning the robot’s tip with and without the additional degree of freedom. The ability to enable continuous rotation after positioning at different points was also tested to demonstrate its performance in various potential applications.