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We present a magnetic camera system developed to detect ferrous or ferromagnetic objects. The main motivation is detection and tracking of underwater pipelines. Many industries, such as oil and gas, must perform inspection and maintenance of pipelines and automation is desirable. An electromagnet generates a static magnetic field which is read by an array of Hall-effect sensors. The presence of ferromagnetic materials distorts this field, which can be detected by the sensors and creates a magnetic image. The grid configuration of the camera allows for quick computation of the center of mass and general orientation of detected pipes, facilitating tracking. This camera is carried by an ROV and tested in a pool environment. 

Many potential medical applications for magnetically controlled tetherless devices inside the human body have been proposed, including procedures such as biopsies, blood clot removal, and targeted drug delivery. These devices are capable of wirelessly navigating through fluid-filled cavities in the body, such as the vascular system, eyes, urinary tract, and ventricular system, to reach areas difficult to access via conventional methods. Once at their target location, these devices could perform various medical interventions. This paper focuses on a special type of magnetic tetherless device called a magnetic rotating swimmer, which has internal magnets and propeller fins with a helical shape. To facilitate the design process, an automated geometry generation program using OpenSCAD was developed to create the swimmer design, while computational fluid dynamics simulations using OpenFOAM were employed to calculate the propulsive force produced by the swimmer. Furthermore, an experimental approach is proposed and demonstrated to validate the model. The results show good agreement between simulations and experiments, indicating that the model could be used to develop an automatic geometry optimization pipeline for rotating swimmers. 

Millimeter-scale magnetic rotating swimmers have multiple potential medical applications. They could, for example, navigate inside the bloodstream of a patient toward an occlusion and remove it. Magnetic rotating swimmers have internal magnets and propeller fins with a helical shape. A rotating magnetic field applies torque on the swimmer and makes it rotate. The shape of the swimmer, combined with the rotational movement, generates a propulsive force. Visual feedback is suitable for in-vitro closed-loop control. However, in-vivo procedures will require different feedback modalities due to the opacity of the human body. In this paper, we provide new methods and tools that enable the 3D control of a magnetic swimmer using a 2D ultrasonography device attached to a robotic arm to sense the swimmer’s position. We also provide an algorithm that computes the placement of the robotic arm and a controller that keeps the swimmer within the ultrasound imaging slice. The position measurement and closed-loop control were tested experimentally. 

Miniature Magnetic Rotating Swimmers (MMRSs) are untethered machines containing magnetic materials. An external rotating magnetic field produces a torque on the swimmers to make them rotate. MMRSs have propeller fins that convert the rotating motion into forward propulsion. This type of robot has been shown to have potential applications in the medical realm. This paper presents new MMRS designs with (1) an increased permanent magnet volume to increase the available torque and prevent the MMRS from becoming stuck inside a thrombus; (2) new helix designs that produce an increased force to compensate for the weight added by the larger permanent magnet volume; (3) different head drill shape designs that have different interactions with thrombi. The two best MMRS designs were tested experimentally by removing a partially dried 1-hour-old thrombus with flow in a bifurcating artery model. The first MMRS disrupted a large portion of the thrombus. The second MMRS retrieved a small remaining piece of the thrombus. In addition, a tool for inserting, retrieving, and switching MMRSs during an experiment is presented and demonstrated. Finally, this paper shows that the two selected MMRS designs can perform accurate 3D path-following. 

Magnetic induction localization is an inverse problem that determines the relative position and orientation (pose) between transmitting and receiving coils by analyzing the received signals. Related work has established methods to resolve the localization into two candidate poses. However, these methods require having signed signals, or periodic signals whose starting point is unambiguously determined with respect to an absolute reference (the transmitted signal). For distributed systems, the signal signs are difficult to resolve. This paper presents a method to extract partial information about the signs from unsigned signals. The method is tested in a hardware experiment. 

This work presents an online trajectory generation algorithm using a sinusoidal jerk profile. The generator takes initial acceleration, velocity and position as input, and plans a multi-segment trajectory to a goal position under jerk, acceleration, and velocity limits. By analyzing the critical constraints and conditions, the corresponding closed-form solution for the time factors and trajectory profiles are derived. The proposed algorithm was first derived in Mathematica and then converted into a C++ implementation. Finally, the algorithm was utilized and demonstrated in ROS & Gazebo using a UR3 robot. Both the Mathematica and C++ implementations can be accessed at https://github.com/Haoran-Zhao/Jerk-continuous-online-trajectory-generator-with-constraints.git 

For biomedical applications in targeted therapy delivery and interventions, a large swarm of micro-scale particles (“agents”) has to be moved through a maze-like environment (“vascular system”) to a target region (“tumor”). Due to limited on-board capabilities, these agents cannot move autonomously; instead, they are controlled by an external global force that acts uniformly on all particles. In this work, we demonstrate how to use a time-varying magnetic field to gather particles to a desired location. We use reinforcement learning to train networks to efficiently gather particles. Methods to overcome the simulation-to-reality gap are explained, and the trained networks are deployed on a set of mazes and goal locations. The hardware experiments demonstrate fast convergence, and robustness to both sensor and actuation noise. To encourage extensions and to serve as a benchmark for the reinforcement learning community, the code is available at Github. 

Reconfigurable modular robots can dynamically assemble/disassemble to accomplish the desired task better. Magnetic modular cubes are scalable modular subunits with embedded permanent magnets in a 3D-printed cubic body and can be wirelessly controlled by an external, uniform, timevarying magnetic field. This paper considers the problem of self-assembling these modules into desired 2D polyomino shapes using such magnetic fields. Although the applied magnetic field is the same for each magnetic modular cube, we use collisions with workspace boundaries to rearrange the cubes. We present a closed-loop control method for self-assembling the magnetic modular cubes into polyomino shapes, using computer vision-based feedback with re-planning. Experimental results demonstrate that the proposed closed-loop control improves the success rate of forming 2D user-specified polyominoes compared to an open-loop baseline. We also demonstrate the validity of the approach over changes in length scales, testing with both 10mm edge length cubes and 2.8mm edge length cubes. 

This paper presents four data-driven system models for a magnetically controlled swimmer. The models were derived directly from experimental data, and the accuracy of the models was experimentally demonstrated. Our previous study successfully implemented two non-model-based control algorithms for 3D path-following using PID and model reference adaptive controller (MRAC). This paper focuses on system identification using only experimental data and a model-based control strategy. Four system models were derived: (1) a physical estimation model, (2, 3) Sparse Identification of Nonlinear Dynamics (SINDY), linear system and nonlinear system, and (4) multilayer perceptron (MLP). All four system models were implemented as an estimator of a multi-step Kalman filter. The maximum required sensing interval was increased from 180 ms to 420 ms and the respective tracking error decreased from 9 mm to 4.6 mm. Finally, a Model Predictive Controller (MPC) implementing the linear SINDY model was tested for 3D path-following and shown to be computationally efficient and offers performances comparable to other control methods.