Untethered magnetic manipulation of biomedical millirobots has a high potential for minimally invasive surgical applications. However, it is still challenging to exert high actuation forces on the small robots over a large distance. Permanent magnets offer stronger magnetic torques and forces than electromagnetic coils, however, feedback control is more difficult. As proven by Earnshaw's theorem, it is not possible to achieve a stable magnetic trap in 3D by static permanent magnets. Here, we report a stable 2D magnetic force trap by an array of permanent magnets to control a millirobot. The trap is located in an open space with a tunable distance to the magnet array in the range of 20 − 120mm, which is relevant to human anatomical scales. The design is achieved by a novel GPU-accelerated optimization algorithm that uses mean squared error (MSE) and Adam optimizer to efficiently compute the optimal angles for any number of magnets in the array. The algorithm is verified using numerical simulation and physical experiments with an array of two magnets. A millirobot is successfully trapped and controlled to follow a complex trajectory. The algorithm demonstrates high scalability by optimizing the angles for 100 magnets in under three seconds. Moreover, the optimization workflow can be adapted to optimize a permanent magnet array to achieve the desired force vector fields.

Surgical planning and training based on machine learning requires a large amount of 3D anatomical models reconstructed from medical imaging, which is currently one of the major bottlenecks. Obtaining these data from real patients and during surgery is very demanding, if even possible, due to legal, ethical, and technical challenges. It is especially difficult for soft tissue organs with poor imaging contrast, such as the prostate. To overcome these challenges, we present a novel workflow for automated 3D anatomical data generation using data obtained from physical organ models. We additionally use a 3D Generative Adversarial Network (GAN) to obtain a manifold of 3D models useful for other downstream machine learning tasks that rely on 3D data. We demonstrate our workflow using an artificial prostate model made of biomimetic hydrogels with imaging contrast in multiple zones. This is used to physically simulate endoscopic surgery. For evaluation and 3D data generation, we place it into a customized ultrasound scanner that records the prostate before and after the procedure. A neural network is trained to segment the recorded ultrasound images, which outperforms conventional, non-learning-based computer vision techniques in terms of intersection over union (IoU). Based on the segmentations, a 3D mesh model is reconstructed, and performance feedback is provided.

Early detection of gastrointestinal (GI) cancer is critical for improving treatment outcomes and survival rates. Yet conventional endoscopic techniques remain invasive and labor-intensive, thus presenting significant challenges for cancer screening on large populations. Current commercially available sponge-based sampling devices are passive and limited in their reach to the esophagus, hindering comprehensive sampling in the stomach. Here, for the first time, we report the SpongeBot – a non-invasive soft mini-robot designed for active cell sampling in the upper GI tract, with a particular focus on the stomach. The SpongeBot integrates an open-cell sponge and a magnetic actuator, enabling precise and controlled sampling under a wireless external magnetic field. To accommodate the intricate anatomy of the stomach, the robot is capable of transitioning between two modes of motion — the navigation and the sampling mode, allowing trajectory control and targeted sampling at desired locations. Kinematic model is established to accurately represent the locomotion of the robot on wet mucosa surfaces. Pilot testing on ex vivo porcine stomachs is successfully performed with sufficient cells sampled for subsequent clinical laboratory testing. Histological analysis shows the sampling causes no detectable damage to the mucosa layer. SpongeBot has the potential as a cell sampling device for the upper GI tract to be deployed in primary care settings for cancer prevention.

Magnetic helical micro-robots (microbots) have attracted strong interest due to their unique propulsion mechanisms and potential applications in biomedical fields, particularly in minimally-invasive surgical procedures. Earlier research primarily focused on studying helical microbots in viscous liquids, while their dynamic behavior in viscoelastic solids remains largely unexplored. Here, we present an experimental study of a helical microbot operating in a viscoelastic gelatin hydrogel. The robot is fabricated by two-photon polymerization and actuated by an external rotating magnetic field. We observe that in viscoelastic solids, the robot ruptures the gel and creates a three-dimensional (3D) helical trajectory, despite the rotational axis of the driving magnetic field being fixed. Largely distinct from the propulsion behavior in a Newtonian fluid, the precession angle of the helix is significantly enhanced in the viscoelastic gel and increases with a rising rotational frequency. A dynamic model is developed using the multipole expansion method, incorporating the gel’s complex viscosity and shear-thinning properties to capture the key characteristics of this dynamic response. These findings offer new insights into the behavior of helical microbots in viscoelastic media, expanding possible application scenarios of microbots in biomedicine.

This paper presents a novel design and experimental investigation for a self-propelled capsule robot that can be used for painless colonoscopy during a retrograde progression from the patient's rectum. The steerable robot is driven forward and backward via its internal vibration and impact with orientation control by using an electromagnetic actuator. The actuator contains four sets of coils and a shaft made by permanent magnet. The shaft can be excited linearly in a controllable and tilted angle, so guide the progression orientation of the robot. Two control strategies are studied in this work and compared via simulation and experiment. Extensive results are presented to demonstrate the progression efficiency of the robot and its potential for robotic colonoscopy.