Magnetic helical miniature robots (MHMRs) exhibit efficient motion performance in low Reynolds number environments, having great promise for biomedical applications like targeted delivery. However, during targeted delivery, the backward propulsion of MHMRs in previous work leads to cargo being released, limiting their degrees of freedom and interference resistance. Furthermore, the basic magnetic field parameter, amplitude, has not been effectively utilized in previous MHMRs. In this letter, we propose a magnetic helical miniature robot with soft magnetic-controlled gripper (MHMR-G), using magnetic field amplitude to functionalize MHMRs for the first time. The velocity of MHMR-G is controlled by magnetic field frequency and the grasping of gripper is controlled by magnetic field amplitude. It is proposed that the lag angle and rotation frequency will adversely affect the grasping of gripper under a rotating magnetic field, but results show that increasing magnetic field amplitude can effectively mitigate these adverse effects. Finally, a manipulation test of cargo transport is performed, demonstrating that the gripper of MHMR-G can effectively confine cargo during propulsion.

Molecular communication (MC) aims to use signaling molecules as information carriers to achieve communication between biological entities. However, MC systems severely suffers from inter symbol interference (ISI) and external noise, making it virtually difficult to obtain accurate mathematical models. Specifically, the mathematically intractable channel state information (CSI) of MC motivates the deep learning (DL) based signal detection methods. In this paper, a modified temporal convolutional network (TCN) is proposed for signal detection for a special MC communication system which uses magnetotactic bacteria (MTB) as information carriers. Results show that the TCN-based detector demonstrates the best overall performance. In particular, it achieves better bit error rate (BER) performance than sub-optimal maximum a posteriori (MAP) and deep neural network (DNN) based detectors. However, it behaves similar with the bidirectional long short term memory (BiLSTM) based detector that have been previously proposed and worse than the optimal MAP detector. When both BER performance and computational complexity are taken into account, the proposed TCN-based detector outperforms BiLSTM-based detectors. Furthermore, in terms of robustness evaluation, the proposed TCN-based detector outperforms all other DL-based detectors.

Molecular communication (MC) enables communication at the nanoscale where traditional electromagnetic waves are ineffective, and accurate signal detection is essential for practical implementation. However, due to the lack of accurate mathematical models, statistical-based signal detection methods are not applicable, and existing deep learning-based models exhibit relative simplicity in design. This paper integrates ideas from natural language processing into MC and proposes the MCFormer, a detector based on the classical Transformer model. Additionally, we propose an accelerated particle-based simulation algorithm using matrix operations for rapid generation of high-quality training data with a lower complexity than traditional methods. The experimental results demonstrate that the MCFormer achieves nearly optimal accuracy in a noise-free environment, surpassing the performance of the Deep Neural Network (DNN). Moreover, MCFormer can show optimal performance in environments with significant levels of unknown noise. All the codes can be found at https://github.com/Xiwen-Lu/MCFormer.

Inspired by bacterial motility mechanisms, Magnetic Helical Miniature Robots (MHMRs) exhibit promising applications in biomedical fields due to their efficient locomotion and compatibility with biological tissues. In this review, we systematically survey the basics of MHMRs, from propulsion mechanism, magnetization and control methods to biomedical applications, aiming to provide readers with an easily understandable overview and fundamental knowledge on implementing MHMRs. The MHMRs are actuated by rotating magnetic fields, achieving steering and rotation through magnetic torque, and converting rotation into forward motion through the helical structure. Magnetization methods for MHMRs are reviewed into three types: attaching magnets, magnetic coatings, and magnetic powder doping. Additionally, this review discusses the control methods for MHMRs, covering imaging techniques, path tracking control—including classical control algorithms and increasingly popular learning-based methods, and swarm control. Subsequently, a comprehensive survey is conducted on the biomedical applications of MHMRs in the treatment of vascular diseases, drug delivery, cell delivery, and their integration with catheters. We finally provide a perspective about future challenges in MHMR research, including enhancing functional design capabilities, developing swarm-assisted independent control mechanisms, refining in vivo imaging techniques, and ensuring robust biocompatibility for safe medical use.

Minimally invasive surgery (MIS) has become increasingly favored by both patients and surgeons owing to its advantages such as shortened recovery times and reduced surgical trauma. To enhance intraoperative feedback from surgical instruments while minimizing harmful radiation exposure, a wide range of electromagnetic tracking systems (EMTS) has been developed at micro scales for medical applications. This review provides a comprehensive summary of advances in the field over the past five years, with an emphasis on the working principles of EMTS, system architecture, current research progress, and clinical applications. In comparison to other review papers, this article focuses specifically on EMTS for medical micro-devices, such as robotic catheters, endoscopes, and capsule robots. Moreover, Representative research studies and commercial systems are presented along with their clinical implementations, placing greater emphasis on the translation of EMTS into medical applications. Finally, this review outlines and discusses future research directions, highlighting major challenges and potential opportunities for advancing the integration of EMTS into routine clinical workflows.

Autonomous navigation of magnetic microswarms in dynamic and unstructured environments is essential for biomedical applications, such as targeted therapy and minimally invasive interventions. However, existing path planning methods struggle to simultaneously achieve real-time adaptability and path smoothness in dynamic obstacle environments. To address this, we propose a hierarchical Dynamic Rapidly-exploring Random Tree Star (D-RRT*) path planning framework that integrates dynamic step size adjustment, local target selection, and local planning that considers microswarms' turning capabilities and energy optimization. Comparative simulations and experiments validate the effectiveness of the proposed planning framework, and results show that it can significantly improve the planning efficiency, path smoothness, and collision avoidance in complex dynamic scenarios.