Dual-Rate Force-Image Control with Model-Based Orientation Limits for Robotic Ultrasound

Tyler Foster; Qiang Zhang; A B M Tahidul Haque; Anh Thu Nguyen

cs.ROOct 5, 2026

Dual-Rate Force-Image Control with Model-Based Orientation Limits for Robotic Ultrasound

Authors: Tyler Foster, Qiang Zhang, A B M Tahidul Haque, Anh Thu Nguyen

Organizations: Department of Mechanical Engineering, The University of Alabama, Tuscaloosa, AL 35487, USA · Department of Chemical & Biological Engineering, The University of Alabama, Tuscaloosa, AL 35487, USA · Department of Computer Science, The University of Alabama, Tuscaloosa, AL 35487, USA

Abstract

Robotic ultrasound couples a high-rate contact-force loop with slower, delayed image feedback, so image-guided ultrasound probe rotation can perturb contact force before the resulting image response is observed. We derive a closed-form orientation-rate limit that bounds the modeled rotation-induced estimated-force excursion over a finite horizon while accounting for disturbance rejection by the fast force loop. The limit depends on local contact stiffness, force-loop gains, a conservative rotation-to-force gain bound, the excursion budget, and the prediction horizon. We implement this model in a dual-rate controller with timestamp-based delay reconstruction and joint-torque-based force estimation, and evaluate it on a curved gelatin phantom using paired controller comparisons and component ablations. Relative to unconstrained image guidance, the proposed rate-limited controller reduced first-second root-mean-square (RMS) estimated-force error by 0.40 N while increasing cue-convergence time by 0.94 s. A fixed rate cap near the analytically predicted ceiling produced no resolvable difference in force error and converged 0.32 s faster, indicating that the principal practical value of the model is the rate-design rule rather than online prediction. Delay reconstruction had no resolvable effect at the tested latency. A single-subject popliteal scan demonstrated feasibility, although the image cue was noise-limited on heterogeneous tissue.

Figures & tables

Fig. 1: Experimental setup: a Kinova Gen3 six-degree-of-freedom arm holds a Telemed LF9-5N60-A3 linear probe in a printed holder against the gelatin phantom; the host machine runs the controller and shows the live B-mode stream and its confidence map.

Fig. 2: Image cue on the phantom. (a) B-mode frame; the right side of the image is darker. (b) Its confidence map $c(r,\kappa)$ (blue $0$ to red $1$ ), with the depth $d(\kappa)$ at which confidence falls below $c_{\rm th}=0.5$ (white) and its least-squares line (dashed). The confidence threshold is reached at shallower depth on the right, so $d(\kappa)$ falls with $x_{\kappa}$ and, by ( 6 ), $I=+0.0977$ . Upper half of the image shown.

Fig. 3: Dual-rate control architecture. Dashed orange: once per image frame ( $30$ Hz); solid blue: every $1$ kHz tick; gray: physical and sensor paths. The cue is filtered and reconstructed to the present instant, the orientation command is scaled through the force-excursion constraint, and the force loop regulates the estimated contact force; $v_{s}$ is the constant $5$ mm/s scan feed.

A $-$ B	$\Delta E_{1}$ (N)	$\Delta E$ (N)	$\Delta t_{I50}$ (s)
B1 $-$ B2	$-1.95^{*}$	$-2.07^{*}$	$+6.66^{*}$
	$[-2.08,-1.82]$	$[-2.19,-1.95]$	$[+5.91,+7.40]$
B2 $-$ P	$+0.40^{*}$	$+0.40^{*}$	$-0.94^{*}$
	$[+0.13,+0.67]$	$[+0.30,+0.51]$	$[-1.04,-0.84]$
P nb $-$ P	$+0.49^{*}$	$+0.54^{*}$	$-0.93^{*}$
	$[+0.29,+0.69]$	$[+0.33,+0.75]$	$[-1.00,-0.86]$

TABLE I: Paired differences between conditions (A

-

B), mean and paired-

t

95% CI,

n=10

pairs each.

Panel of Figure 5 — Fig. 4: Engagement and cue convergence after release ( $t=0$ ) on the phantom. (a) Running RMS of force error (100 ms centered window); shading marks the per-horizon budget $\Delta F=0.5$ N. (b) Filtered cue magnitude over its mean in the first $30$ ms, $|I|/I_{\mathrm{ref}}$ ; dotted lines mark 50% ( $t_{I50}$ ) and 20%. Line styles as in (a). Curves are medians over the 10 paired runs of each condition in one block (B2: B2/P; P and B2 cap : B2 cap /P; P 1s : P 1s /P fix ; B1: B1/B2), with interquartile bands. Offsets between curves from different blocks are not paired comparisons; the paired comparisons are in Table I . P nb and P fix (omitted) overlap B2 and P, respectively.

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Sep 28, 2026, cs.ROJ/K move · Enter open · S save

Contact-Aware Impedance Controller for Robot-Assisted Ultrasound Imaging

MD Miraj Arefin, M Efe Tiryaki

Center for Robotics and AI (ROMER), Middle East Technical University, Ankara, Turkey

Robotic Ultrasound Impedance Control Ground Reaction Forces Soft Robotics Acoustic

Open paper AskPDF arXiv

Safe robot-assisted ultrasound imaging requires a reliable controller able to detect and localize probe--tissue interaction. In this paper, we present a B-mode ultrasound image-based contact perception method and a contact-aware impedance controller for robotic ultrasound imaging. The proposed method detects acoustic contact independently of force measurements, enabling contact-conditioned force/torque taring to reduce residual wrench bias. During contact, the method continuously estimates the effective contact location along the curved probe surface and uses it to update the controller interaction frame, enabling visual servoing of the physical probe--tissue contact point during imaging. Experiments on an agar phantom demonstrated a contact-localization RMSE of

\mathbf{1.46 \pm 0.14}

~mm over probe roll angles from

\mathbf{-15^\circ}

\mathbf{15^\circ}

. During static rolling, the proposed controller maintained task-space tracking accuracy comparable to a conventional fixed-frame impedance controller while reducing the maximum compressive interaction force from

\mathbf{31.56}

~N to

\mathbf{20.09}

~N, corresponding to a

\mathbf{36.3\%}

reduction. These results demonstrate the potential of ultrasound images as direct contact feedback for safe and accurate robot-assisted ultrasound imaging.

Abstract

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Contact-Aware Impedance Controller for Robot-Assisted Ultrasound Imaging

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