Control of Magnetic Surgical Robots With Model-Based Simulators and Reinforcement Learning

Barnoy, Yotam; Erin, Önder; Raval, Suraj; Pryor, Will; Mair, Lamar O.; Weinberg, I.; Diaz-Mercado, Yancy; Krieger, Axel; Hager, Gregory D.

doi:10.1109/tmrb.2022.3214426

Cited by 4 publications

(2 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Though these methods have shown success in achieving accurate target placement of the needle tip, they rely on finite element and biomehanics models. Accurate characterization of needle-tissue dynamics can be time consuming, and thus, it can be challenging to implement real-time control Barnoy et al, 2022).…”

Section: Figurementioning

confidence: 99%

Model-free control for autonomous prevention of adverse events in robotics

Narayan,

Majewicz Fey

2024

Front. Robot. AI

View full text Add to dashboard Cite

Introduction: Preventive control is a critical feature in autonomous technology to ensure safe system operations. One application where safety is most important is robot-assisted needle interventions. During incisions into a tissue, adverse events such as mechanical buckling of the needle shaft and tissue displacements can occur on encounter with stiff membranes causing potential damage to the organ.Methods: To prevent these events before they occur, we propose a new control subroutine that autonomously chooses a) a reactive mechanism to stop the insertion procedure when a needle buckling or a severe tissue displacement event is predicted and b) an adaptive mechanism to continue the insertion procedure through needle steering control when a mild tissue displacement is detected. The subroutine is developed using a model-free control technique due to the nonlinearities of the unknown needle-tissue dynamics. First, an improved version of the model-free adaptive control (IMFAC) is developed by computing a fast time-varying partial pseudo derivative analytically from the dynamic linearization equation to enhance output convergence and robustness against external disturbances.Results and Discussion: Comparing IMFAC and MFAC algorithms on simulated nonlinear systems in MATLAB, IMFAC shows 20% faster output convergence against arbitrary disturbances. Next, IMFAC is integrated with event prediction algorithms from prior work to prevent adverse events during needle insertions in real time. Needle insertions in gelatin tissues with known environments show successful prevention of needle buckling and tissue displacement events. Needle insertions in biological tissues with unknown environments are performed using live fluoroscopic imaging as ground truth to verify timely prevention of adverse events. Finally, statistical ANOVA analysis on all insertion data shows the robustness of the prevention algorithm to various needles and tissue environments. Overall, the success rate of preventing adverse events in needle insertions through adaptive and reactive control was 95%, which is important toward achieving safety in robotic needle interventions.

show abstract

Section: Figurementioning

confidence: 99%

Model-free control for autonomous prevention of adverse events in robotics

Narayan,

Majewicz Fey

2024

Front. Robot. AI

View full text Add to dashboard Cite

show abstract

“…Recently, artificial neural networks have been combined with proportional resonant differential feed-forward control methods for controlling currents in coil arrays aimed at supplying rotational fields to magnetic robots, demonstrating improved control of the robot's position and rotation with extremely small error [30]. Previous work using model-free reinforcement learning aimed at teaching a system to guide a needle using arrays of electromagnets [31] or to suspend a magnetic bead in a fluid against the force of gravity [32,33].…”

Section: Introductionmentioning

confidence: 99%

A Control Interface for Autonomous Positioning of Magnetically Actuated Spheres Using an Artificial Neural Network

Huynh,

Mutawak,

et al. 2024

Robotics

Self Cite

View full text Add to dashboard Cite

Electromagnet arrays show significant potential in the untethered guidance of particles, devices, and eventually robots. However, complications in obtaining accurate models of electromagnetic fields pose challenges for precision control. Manipulation often requires the reduced-order modeling of physical systems, which may be computationally complex and may still not account for all possible system dynamics. Additionally, control schemes capable of being applied to electromagnet arrays of any configuration may significantly expand the usefulness of any control approach. In this study, we developed a data-driven approach to the magnetic control of a neodymium magnets (NdFeB magnetic sphere) using a simple, highly constrained magnetic actuation architecture. We developed and compared two regression-based schemes for controlling the NdFeB sphere in the workspace of a four-coil array of electromagnets. We obtained averaged submillimeter positional control (0.85 mm) of a NdFeB hard magnetic sphere in a 2D plane using a controller trained using a single-layer, five-input regression neural network with a single hidden layer.

show abstract