Magnetically actuated microswimmers have attracted researchers to investigate their swimming characteristics and controlled actuation. Although plenty of studies on actuating helical microswimmers have been carried out, robust closed-loop controls should be still explored for practical applications. In this paper, we proposed a data-driven model-free method using Image-Based Visual Servoing (IBVS), which uses features directly extracted in the image space as feedbacks. The IBVS method can eliminate camera calibration errors. We have demonstrated with experiments that the proposed IBVS method can enable velocity-independent path following of an arbitrarily given path on the plane, which permits a better experience of user interaction. The proposed control method is successfully applied to obstacle avoidance tasks and has the potential for the application in complex circumstances. This approach is promising for biomedical applications.

As the controller parameter adjustment process is simplified significantly by using learning algorithms, the studies about learning-based control attract a lot of interest in recent years. This article focuses on the intelligent servo control problem using learning from desired demonstrations. Compared with the previous studies about the learning-based servo control, a control policy using the broad learning system (BLS) is developed and first applied to a microrobotic system, since the advantages of the BLS, such as simple structure and no-requirement for retraining when new demos’ data is provided. Then, the Lyapunov theory is skillfully combined with the complex learning algorithm to derive the controller parameters’ constraints. Thus, the final control policy not only can obtain the movement skills of the desired demonstrations but also have the strong ability of generalization and error convergence. Finally, simulation and experimental examples verify the effectiveness of the proposed strategy using MATLAB and a microswimmer trajectory tracking system.

The combination of electronic health records (EHR) and medical images is crucial for clinicians in making diagnoses and forecasting prognoses. Strategically fusing these two data modalities has great potential to improve the accuracy of machine learning models in clinical prediction tasks. However, the asynchronous and complementary nature of EHR and medical images presents unique challenges. Missing modalities due to clinical and administrative factors are inevitable in practice, and the significance of each data modality varies depending on the patient and the prediction target, resulting in inconsistent predictions and suboptimal model performance. To address these challenges, we propose DrFuse to achieve effective clinical multi-modal fusion. It tackles the missing modality issue by disentangling the features shared across modalities and those unique within each modality. Furthermore, we address the modal inconsistency issue via a disease-wise attention layer that produces the patient- and disease-wise weighting for each modality to make the final prediction. We validate the proposed method using real-world large-scale datasets, MIMIC-IV and MIMIC-CXR. Experimental results show that the proposed method significantly outperforms the state-of-the-art models.

Steering microswimmers toward 3-D autonomous manipulation tasks has received extensive attention. Our previous works have accomplished autonomously manipulating microswimmers in the 2-D space. This article aims to extend the 2-D autonomous manipulation to 3-D autonomous manipulation. Specifically, this article addresses the problem of an autonomous system that consists of 3-D path planning and 3-D path following for magnetically driven helical microswimmers. The path-planning algorithm called optimal Bidirectional RRT* is formulated to explore the shortest route in the confined 3-D space. A proxy-based sliding mode control (PSMC) approach is developed to design stable controllers based on the error model in the Serret–Frenet frame. We transport the swimming model trained by a kind of neural network to another new helical microswimmer according to an online updating scheme. The updating scheme can identify and refine compensating angles between the swimming direction of the microswimmer and the magnetic direction in the 3-D space facing the weight disturbances of the swimmer and lateral disturbances. The experiments are conducted to quantitatively validate the 3-D autonomous manipulation system. Experimental results show the effectiveness of path planning and path following with submillimeter accuracy in a 3-D space. Future works will focus on autonomous manipulations in dynamic environments. Note to Practitioners —This article is motivated by the issue of 3-D autonomous manipulation tasks for magnetically driven helical microswimmers. The formulated path planning is responsible for finding the shortest route in the 3-D confined space. The closed-loop controller is charge of steering the helical microswimmers on a reference path based on an online updating model trained by neural networks. It is demonstrated that the helical microswimmer can find the shortest path and follow it in a 3-D space with submillimeter accuracy.

Autonomous vehicles have received considerable attention, yet high-speed path following control remains a critical and challenging issue. At high speeds, achieving perfect control based on an accurate dynamic model of autonomous vehicles is difficult due to the nonlinear model parameters that change at the driving limit. Moreover, the strong coupling relationship between longitudinal control and lateral control also affects control performance. To address these challenges, a reinforcement learning-based control approach (RL-controller) is developed in this paper and applied to the high-speed path following task. The proposed RL-controller overcomes the challenges of explicit modeling and tight coupling control. Firstly, to obtain efficient learning features, our RL-controller is developed using a deep soft actor-critic method that incorporates multi-layer dense connection networks with skip connections and feature reuse. Secondly, an estimation method of the lane curvature is developed, which is chosen as prior knowledge to augment the input state of reinforcement learning. We model the lane using a transformer-based network structure that utilizes a third-order polynomial and train the model on our self-collected dataset. Thirdly, intuitive reward functions are built from the cross-track error, the angle error and the sideslip angle. At last, we verify our proposed RL-controller utilizing an open autonomous driving simulator. Compared with imitation learning and model-based optimal control methods, our RL-controller outperforms them in terms of the driving speed and compound error.