DexterousMag: A Reconfigurable Electromagnetic Actuation System for Miniature Helical Robot

Abstract

Despite the promise of magnetically actuated miniature helical robots for minimally invasive interventions, state-of-the-art electromagnetic actuation systems are often space-inefficient and geometrically fixed. These constraints hinder clinical translation and, moreover, prevent task-adaptive trade-offs among workspace coverage, energy distribution, and field/gradient capability. We present DexterousMag, a robot-arm-assisted three-coil electromagnetic actuation system that enables continuous geometric reconfiguration of a compact coil group, thereby redistributing magnetic-field and gradient capability for task-adaptive operation. The reconfiguration is realized by a parallel mechanism that exposes a single geometric DOF of the coil group, conveniently parameterized by the polar angle. Using an FEM-based modeling pipeline, we precompute actuation and gradient libraries and quantify the resulting trade-offs under current limits: configurations that favor depth reach expand the feasible region but reduce peak field/gradient, whereas configurations that favor near-surface capability concentrate stronger fields/gradients and support lifting. We validate these trade-offs on representative tasks (deep translation, planar tracking, and 3D lifting) and further demonstrate a proof-of-concept online geometry scheduling scheme for combined tasks, benchmarked against fixed-geometry settings. Overall, DexterousMag establishes continuous geometric reconfiguration as an operational mechanism for enlarging the practical envelope of miniature helical robot actuation while improving energy efficiency and safety.

Figures (5)

• Figure 1: Concept of DexterousMag: a reconfigurable, robot-arm-assisted electromagnetic actuation system that redistributes magnetic energy and gradient capability through continuous coil-group reconfiguration (parameterized by $\theta$) for task-adaptive control of a miniature helical robot for biomedical treatment.
• Figure 2: DexterousMag overview. (a) Schematic of the hardware and signal architecture. (b) Exploded view of the proposed mechanism that converts stepper–motor rotation into changes of the coil-group polar angle $\theta$. (c) Assembly diagram; plane $P$ denotes the cross-section of one coil mechanism. (d) One-coil mechanism: a planar four-bar linkage in plane $P$. (e) Modeling pipeline for building offline libraries of the actuation matrix $\mathbf A(\mathbf p,\theta)$ and gradient Jacobian $\mathbf {\mathcal{G} (\mathbf p,\theta)}$, enabling offline workspace analysis and online magnetic-field synthesis.
• Figure 3: Simulated workspace analysis across $\theta$. (a) Energy density $\log u$ at $\theta=35^\circ,45^\circ,55^\circ$. (b) Mean $\log u$ vs. depth $z$ (at $x{=}y{=}0$). (c) Feasible workspace $\mathcal{W}_\theta$ colored by $B_{\min}$. (d) Effective workspace volume $V(\theta)$ (convex hull). (e) Gradient disturbance map $g_F{=}\|\nabla\mathbf B\|_F$ vs. $z$ and elevation $\varepsilon$ under $B_0{=}1$ mT; azimuth is averaged due to threefold symmetry. (f) Cycle-averaged vertical force $F_z$ vs. $z$ at $(x,y)=(0,0)$, $\varepsilon{=}0$. All panels use common color scales and current limits for across $\theta$ comparability.
• Figure 4: Experiments 1-3 on the physical DexterousMag. a) Three mechanism configurations at $\theta=35^\circ,45^\circ,55^\circ$. b) Exp. 1: Straight line in a tube. Let $B_0^{\star}$ denote the critical field magnitude at the target point at which the miniature helical robot transitions from static to accelerated motion. We compare the total coil current required to reach $B_0^{\star}$ across $\theta$: smaller $\theta$ attains the same depth with less current, indicating greater depth reach per ampere. c) Exp. 2: Circular tracking in free space ($B=1\,\mathrm{mT}$): $\theta\approx45^\circ$ yields the lowest planar tracking error with comparable current to $35^\circ$, while $55^\circ$ degrades tracking due to stronger, more disruptive gradients. d) Exp. 3: Straight $z$-ramping in free space: $\theta=55^\circ$ provides steeper gradients and the most efficient lifting.
• Figure 5: Experiment 4: Depth-based $\theta$ scheduling in a tortuous tube under an obstacle constraint. (a) Setup: a 3D-printed tortuous tube provides a vessel-like path, and an acrylic plate emulates anatomical thickness and imposes a collision constraint. The trajectory concatenates the three task primitives from Experiments 1-3 (deep reach, cruise, and shallow/climb). (b) Tube views and the depth-triggered schedule showing online updates of $\theta$ based on target robot's height. For fair comparison, we align a common collision-free lower boundary across configurations to decouple $\theta$-dependent geometry from the effect of field redistribution. (c) Results: median $E/B^2$ and $P(I_{\text{peak}}\ge 7~\mathrm{A})$ across depth-triggered auto $\theta$ schedule and fixed 35,45,55 $\theta$