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
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,κ) (blue 0 to red 1 ), with the depth d(κ) at which confidence falls below cth=0.5 (white) and its least-squares line (dashed). The confidence threshold is reached at shallower depth on the right, so d(κ) falls with xκ 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; vs is the constant 5 mm/s scan feed.
A − B
ΔE1 (N)
ΔE (N)
ΔtI50 (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.
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 ΔF=0.5 N. (b) Filtered cue magnitude over its mean in the first 30 ms, ∣I∣/Iref ; dotted lines mark 50% ( tI50 ) 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.
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 1.46±0.14~mm over probe roll angles from −15∘ to 15∘. 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 31.56~N to 20.09~N, corresponding to a 36.3% reduction. These results demonstrate the potential of ultrasound images as direct contact feedback for safe and accurate robot-assisted ultrasound imaging.
MD Miraj Arefin, M Efe Tiryaki
Center for Robotics and AI (ROMER), Middle East Technical University, Ankara, Turkey
Ultrasound (US) provides real-time, radiation-free imaging, but the image quality depends strongly on how the probe is oriented against the patient body. Robotic US can reduce operator workload and improve acquisition consistency; however, most existing systems focus on normal positioning, where the probe is maintained perpendicular to the local surface. This constraint is inadequate for examinations like echocardiography, where obtaining a diagnostic view requires a non-normal probe angle. Consequently, a clinically useful robotic system must sense the local surface in real-time and preserve the desired probe orientation. Here, we propose an omni-directional probe-orientation control framework that integrates RGB-D perception, local-surface modeling, and task-space orientation control. The surface model fuses multi-view point clouds and provides a quadratic estimate of the local surface. A desired imaging direction is then encoded relative to the normal, enabling the probe to track arbitrary angles. The framework was evaluated through flat-surface tracking, phantom target-angle recovery, and in-vivo tracking of an expert selected view. Results show that the mean angular tracking error was 1.06 +- 0.66 deg. The system recovered a non-normal tilt angle of up to 44.39 +- 2.59 deg relative to the surface normal, and acquired the desired heart chamber view in the phantom and in-vivo experiments.
Xihan Ma, Haichong Zhang
Department of Robotics Engineering, Worcester Polytechnic Institute, 100 Institute Road, Worcester, MA 01609 USA
Ultrasound visual servoing is essential for autonomous robotic ultrasound, yet 6-DoF probe control from 2D B-mode images remains challenging due to limited and ambiguous out-of-plane motion cues. Existing methods typically rely on anatomical priors or handcrafted visual features, limiting their generalizability across imaging targets. Inspired by trackerless 3D ultrasound reconstruction, we propose Recon2Servo, a visual servoing framework that learns image-to-motion inference directly from B-mode images for 6-DoF probe control. A DINOv3 encoder with low-rank adaptation and a bidirectional relation module estimate the relative probe pose between current and target images to guide iterative closed-loop target-view alignment. The framework combines supervised relative-pose learning, reconstruction-guided closed-loop adaptation, and bounded residual pose correction to improve motion inference during servoing. Evaluations on a public dataset and an in-house dataset collected from 12 healthy volunteers using different ultrasound systems demonstrate its effectiveness in reconstructed-volume servoing. Additional real-robot demonstrations of target-view alignment and dynamic tracking on a human forearm are provided in the supplementary video: https://youtu.be/qwsOdI-GMYk.
Yameng Zhang, Pei Liu, Dianye Huang +5
Department of Mechanical Engineering, The University of Hong Kong, Hong Kong SAR. · Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong, Hong Kong SAR. · School of Computation, Information and Technology, Technical University of Munich, Germany. +2