Microswarms face challenges in precise delivery within dynamic biological fluids due to fluid disturbances and limited operational intuitiveness. Current approaches insufficiently utilize the operator’s perceptual awareness and interactive decision-making capabilities, particularly in complex tasks that require a balance between flexibility and precision. In this study, we propose a haptic-assisted magnetic actuation control strategy, establishing a human-in-the-loop control framework. The haptic perception system provides the operator with haptic feedback reflecting the interactions between the microswarm and the environment. A real-time tracking system monitors the position and pattern of the controlled microswarm in remote environments, and transmits this information to the control system for decision-making. After characterizing the magnetic field parameters and magnetic nanoparticles, we have achieved real-time navigation and morphology modulation of the microswarm in dynamic flow conditions and three-dimensional (3D) space. Comparative experiments under various flow rate conditions demonstrate that the haptic-assisted strategy enhances microswarm control stability and precision across different flow regimes. Moreover, the human-machine collaboration mechanism improves delivery success rates (97%) under sudden disturbances compared to preprogrammed automated control and purely manual control, validating its potential for applications in complex biomedical scenarios. Our work provides a haptic-assisted microswarm control method in dynamic conditions, expanding an adaptive microswarm control strategy in complex biomedical environments.

Swarming microrobots offer great promise for targeted delivery in biofluidic environments. However, current approaches insufficiently utilize the operator’s perceptual awareness and interactive decision-making capabilities. This work proposes a real-time navigation and control strategy with haptic feedback for delivering magnetic microswarm, in which the haptic feedback system provides microswarm-environment interaction to the operator. The real-time tracking system continuously monitors the position and shape of the microswarm in the remote environment, transmitting data to the control system for decision-making. This integration can achieve real-time perception and feedback of the microswarm’s state and motion process. Moreover, the strategy successfully demonstrates navigation and shape-adaptive regulation of the microswarm under static, downstream and three-dimensional (3D) upstream flow conditions. The experimental results show that the haptic feedback enables real-time trajectory and velocity adjustments during navigation, improving control robustness and delivery accuracy. Our work expands a haptic feedback-enabled microswarm control in dynamic conditions, providing an adaptive swarm control strategy in complex biomedical environments.

Microrobotic swarms have shown promising features due to their collective and flexible behaviours, while achieving precise swarm control and autonomous navigation in complex environments remains a challenge. Here, we propose a Transformer-based reinforcement learning strategy that integrates Proximal Policy Optimization for autonomous swarm control in obstacle environments. By combining domain randomization, this strategy enables direct transfer from simulation to real-world without fine tuning. Experimental results demonstrate robust control performance in avoiding static obstacles and tracking the dynamic target, which is not validated in training. The swarm autonomously navigates and adjusts its velocity and trajectory in obstacle environments with an intact swarm pattern. Our work presents a scalable strategy for the deployment of microrobotic swarms with adaptive navigation capability through complex, constrained environments.

Collective microrobots enable controlled batch delivery, showing promising application in the biomedical field. However, significant challenges remain in achieving long-distance delivery of collective microrobots in dynamic environments. This study proposes a magnetic actuation strategy for delivering collective cell microrobots in flowing conditions. A magnetic actuation method is developed, and a mobile actuation system with multiple coils coordination is designed to generate spatially isotropic magnetic fields. Experiments of delivering collective microrobots are conducted in flowing conditions, including downstream and upstream with an average flow velocity up to 8.84 mm/s. Results demonstrate that the proposed actuation strategy enhances driving performance in dynamic environments, achieving long-distance delivery of collective microrobots (over 548 mm). The final access rate of microrobots reaches 90.63% and 94.79% in upstream and downstream conditions, respectively. Our strategy provides an efficient control method for delivering collective microrobots, showing potential for targeted delivery in biomedical applications.

We propose a solution to Fourier ptychographic microscopy (FPM) by combining a no-reference image quality assessment module based on information entropy (IENR-IQA) and a physics-based neural network (PbNN) to achieve rapid reconstruction from multiple low-resolution images to highresolution images. This improves the reconstruction and makes it more generalizable and robust than the traditional FPM methods. Existing reconstruction methodologies are susceptible to systematic errors, including pupil aberration and light-emitting diode (LED) positional intensity discrepancies, which profoundly impact reconstruction clarity and color fidelity. In response, a PbNN featuring image quality evaluation is introduced for FPM reconstruction in this paper. A key innovation is incorporating the IENR-IQA module within a PbNN, to facilitate adaptive correction of spatial variations in LED intensity and position. In addition, the IENR-IQA module employs a fully connected layer to rectify pupil aberration. Rigorous simulations and experiments were conducted to validate the proposed method’s effectiveness and robustness. Experimental results demonstrate that the proposed IENR-IQA model can predict image quality well. When combined with PbNN in FPM, the image quality is improved compared to the existing physical neural networks, making the reconstruction results closer to human vision perception. The proposed IENR-IQA PbNN enhances the possibilities for applying the FPM technology in practice.