Inspired by the collective intelligence in natural swarms, microrobotic agents have been controlled to form artificial swarms for targeted drug delivery, enhanced imaging, and hyperthermia. Different from these well-investigated tasks, this work aims to develop microrobotic swarms for embolization, which is a clinical technique used to block blood vessels for treating tumors, fistulas, and arteriovenous malformations. Magnetic particle swarms were formed for selective embolization to address the low selectivity of the present embolization technique that is prone to cause complications such as stroke and blindness. We established an analytical model that describes the relationships between fluid viscosity, flow rate, branching angle, magnetic field strength, and swarm integrity, based on which an actuation strategy was developed to maintain the swarm integrity inside a targeted region under fluidic flow conditions. Experiments in microfluidic channels, ex vivo tissues, and in vivo porcine kidneys validated the efficacy of the proposed strategy for selective embolization.

Jumping is an important locomotion function to extend navigation range, overcome obstacles, and adapt to unstructured environments. In that sense, continuous jumping and direction adjustability can be essential properties for terrestrial robots with multimodal locomotion. However, only few soft jumping robots can achieve rapid continuous jumping and controlled turning locomotion for obstacle crossing. Here, we present an electrohydrostatically driven tethered legless soft jumping robot capable of rapid, continuous, and steered jumping based on a soft electrohydrostatic bending actuator. This 1.1 g and 6.5 cm tethered soft jumping robot is able to achieve a jumping height of 7.68 body heights and a continuous forward jumping speed of 6.01 body lengths per second. Combining two actuator units, it can achieve rapid turning with a speed of 138.4° per second. The robots are also demonstrated to be capable of skipping across a multitude of obstacles. This work provides a foundation for the application of electrohydrostatic actuation in soft robots for agile and fast multimodal locomotion.

An unmanned aerial vehicle (UAV) swarm that self-organizes to provide superior intelligence and overwhelming effects is a promising technology in urban scenarios. Complex terrain constraints of urban environment increase mission planning difficulties, and bring a strong input-output coupling between subproblems of mission planning, which affect the computing performance and effectiveness of the UAV swarm. In this paper, an evolutionary utility prediction matrix (EUPM) method is presented to solve the input-output-coupled mission planning problem for a UAV swarm executing heterogeneous tasks in an urban scenario with complex constraints. A structured framework of urban missions is established with a distributed mission planning architecture. The input-output coupling relationships between the subproblems of mission planning are analyzed in terms of typical urban mission patterns. Modules for the mission planning subproblems are designed with improved input-output coupling relationships. Simulations and hardware-in-loop experiments demonstrate that the EUPM method achieves accurate prediction of the utility and high effective mission planning solutions than the traditional methods.

This article reports an automated optically-induced dielectrophoresis (ODEP) system for characterizing the specific membrane capacitance (SMC) of individual cells. The simulation of cell motion is conducted to analyze the electrokinetic forces acting on the cell. A self-developed visual tracking algorithm for multicells is used to realize an automated process for determining the frequency-sweeping range, crossover frequencies, and cell radii. The SMC values of malignant bladder cancer cells (T24 and RT4) and normal urothelial cells (SV-HUC-1) were quantified using the automated system, demonstrating that the system has a measurement speed of ~ 1 cell/s, an accuracy of 1 kHz for the crossover frequency determination, and an accuracy of 0.2 μm for the cell radius measurement.