Downloads

https://doi.org/10.62836/isrm.v3i1.0001
Chen, P., Yokoi, H., & Zhang, B. (2026). Autonomous Robotic Ultrasound Scanning for Liver Interventions: an Integrated Redundancy Resolution and Motion Control Framework. Intelligent Systems & Robotic Mechanics, 3(1), 0001. https://doi.org/10.62836/isrm.v3i1.0001
Volume 3, Issue 1, 2026

Autonomous Robotic Ultrasound Scanning for Liver Interventions: an Integrated Redundancy Resolution and Motion Control Framework

Ultrasound-guided interventions, such as liver tumor puncture and ablation, are critical procedures that depend fundamentally on the acquisition of stable, high-quality, real-time imaging. Manual ultrasound scanning, however, suffers from significant operator-dependency and motion instability, which can compromise diagnostic accuracy and procedural outcomes. To address these limitations, this paper introduces a novel, integrated motion control framework for a 7-axis redundant robotic arm designed to perform automated, high-fidelity sector scans. Ultrasound-guided interventions, such as liver tumor puncture and ablation, necessitate stable and high-quality real-time imaging for procedural success. However, manual scanning is often hindered by operator dependency and inherent motion instability. This paper presents a novel motion control framework for a 7-DOF redundant robotic arm, specifically optimized for automated, high-fidelity sector scans. The methodology integrates a human-in-the-loop compliant guidance mode with an autonomous optimization-based execution scheme. The workflow enables clinicians to manually position the probe via a free-drive mode, after which the system computes a kinematically optimal trajectory by considering clinical ergonomics and joint limit avoidance. A real-time, Jacobian-based redundancy resolution controller is implemented to translate task-space trajectories into smooth joint-level commands, incorporating a null-space optimization strategy with a Clinical Dexterity & Safety Index (CDSI) to ensure avoidance of singularities and joint limits. Experimental results demonstrate that the proposed system achieves sub-millimeter tracking accuracy with a mean position error of <0.3 mm and a multi-axis orientation error of <1 deg. Furthermore, compared to standard SDK-based methods, our framework significantly enhances motion smoothness, reducing end-effector jerk by 30% and maintaining a higher directional manipulability (w_scan) throughout the scanning phase. This framework successfully transitions from manual guidance to automated execution, ensuring highly repeatable and stable ultrasound imaging for critical interventional procedures.

medical robotics autonomous ultrasound scanning 7-DOF redundant manipulator redundancy resolution clinical dexterity

References

  1. Yang T, Chen Q, Kuang L, et al. Effectiveness and Safety of Ultrasound-Guided High-Intensity Focused Ultrasound Ablation for the Treatment of Colorectal Cancer Liver Metastases. International Journal of Hyperthermia 2022; 39: 829–834. https://doi.org/10.1080/02656736.2022.2086712.
  2. Li N, Dong Y, Ding Y, et al. Comparison of the Efficacy and Safety of Ultrasound-Guided Radiofrequency Ablation and Microwave Ablation for the Treatment of Unifocal Papillary Thyroid Microcarcinoma: A Retrospective Study. International Journal of Hyperthermia 2024; 41: 2287964. https://doi.org/10.1080/02656736.2023.2287964.
  3. Cheng Z, Koskinopoulou M, Bano S, et al. Sensing Technologies for Guidance During Needle-Based Interventions. IEEE Transactions on Instrumentation and Measurement 2024; 73: 4009615. https://doi.org/10.1109/TIM.2024.3441017.
  4. Long Y, Xu E, Zeng Q, et al. Intra-Procedural Real-Time Ultrasound Fusion Imaging Improves the Therapeutic Effect and Safety of Liver Tumor Ablation in Difficult Cases. American Journal of Cancer Research 2020; 10: 2174–2184.
  5. Lin XX, Li MD, Ruan SM, et al. Autonomous Robotic Ultrasound Scanning System: A Key to Enhancing Image Analysis Reproducibility and Observer Consistency in Ultrasound Imaging. Frontiers in Robotics and AI 2025; 12: 1527686. https://doi.org/10.3389/frobt.2025.1527686.
  6. Kennedy VL, Flavell CA, Doma K. Intra-Rater Reliability of Transversus Abdominis Measurement by a Novice Examiner: Comparison of “Freehand” to “Probe Force Device” Method of Real-Time Ultrasound Imaging. Ultrasound 2019; 27: 156–166. https://doi.org/10.1177/1742271X19831720.
  7. Penzkofer T, Bruners P, Isfort P, et al. Free-Hand CT-Based Electromagnetically Guided Interventions: Accuracy, Efficiency and Dose Usage. Minimally Invasive Therapy & Allied Technologies 2011; 20: 226–233. https://doi.org/10.3109/13645706.2011.553256.
  8. Jung EM, Friedrich C, Hoffstetter P, et al. Volume Navigation with Contrast Enhanced Ultrasound and Image Fusion for Percutaneous Interventions: First Results. PLoS ONE 2012; 7: e33956. https://doi.org/10.1371/journal.pone.0033956.
  9. Munir K, Al-Battal AF, Alsheghri A, et al. A Survey of Autonomous Robotic Ultrasound Scanning Systems. IEEE Access 2025; 13: 103178–103197. https://doi.org/10.1109/ACCESS.2025.3574464.
  10. Suligoj F, Heunis CM, Sikorski J, et al. RobUSt–An Autonomous Robotic Ultrasound System for Medical Imaging. IEEE Access 2021; 9: 67456–67465. https://doi.org/10.1109/ACCESS.2021.3077037.
  11. Ginhoux R, Gangloff J, de Mathelin M, et al. Active Filtering of Physiological Motion in Robotized Surgery Using Predictive Control. IEEE Transactions on Robotics 2005; 21: 67–79. https://doi.org/10.1109/TRO.2004.833812.
  12. Jiang Z, Grimm M, Zhou M, et al. Automatic Force-Based Probe Positioning for Precise Robotic Ultrasound Acquisition. IEEE Transactions on Industrial Electronics 2021; 68: 11200–11211. https://doi.org/10.1109/TIE.2020.3036215.
  13. Xie Z, Jin L, Luo X. Kinematics-Based Motion-Force Control for Redundant Manipulators with Quaternion Control. IEEE Transactions on Automation Science and Engineering 2023; 20: 1815–1828. https://doi.org/10.1109/TASE.2022.3186668.
  14. Chatelain P, Krupa A, Navab N. Confidence-Driven Control of an Ultrasound Probe. IEEE Transactions on Robotics 2017; 33: 1410–1424. https://doi.org/10.1109/TRO.2017.2723618.
  15. Wang Z, Zhao B, Zhang P, et al. Full-Coverage Path Planning and Stable Interaction Control for Automated Robotic Breast Ultrasound Scanning. IEEE Transactions on Industrial Electronics 2023; 70: 7051–7061. https://doi.org/10.1109/TIE.2022.3204967.
  16. Duan B, Xiong L, Guan X, et al. Tele-Operated Robotic Ultrasound System for Medical Diagnosis. Biomedical Signal Processing and Control 2021; 70: 102900. https://doi.org/10.1016/j.bspc.2021.102900.
  17. Torres LG, Baykal C, Alterovitz R. Interactive-Rate Motion Planning for Concentric Tube Robots. In Proceedings of the 2014 IEEE International Conference on Robotics and Automation (ICRA), Hong Kong, China, 31 May–7 June 2014; pp. 1915–1921.
  18. Fiore MD, Meli G, Ziese A, et al. A General Framework for Hierarchical Redundancy Resolution Under Arbitrary Constraints. IEEE Transactions on Robotics 2023; 39: 2468–2487. https://doi.org/10.1109/TRO.2022.3232266.
  19. Pagnotta DP, Monteriù A, Freddi A, et al. Redundancy Resolution Scheme for Manipulators Subject to Inequality Constraints. International Journal of Control, Automation and Systems 2023; 21: 575–590. https://doi.org/10.1007/s12555-021-0641-8.
  20. Zhang B, Chen K, Yao Y, et al. Semi-Automatic Puncture Robotic System Based on Real-Time Multi-Modal Image Fusion: Preclinical Evaluation. International Journal of Computer Assisted Radiology and Surgery 2025; 20: 2479–2489. https://doi.org/10.1007/s11548-025-03471-5.
  21. Albu-Schäffer A, Haddadin S, Ott C, et al. The DLR Lightweight Robot: Design and Control Concepts for Robots in Human Environments. Industrial Robot 2007; 34: 376–385. https://doi.org/10.1108/01439910710774386.
  22. Li K, Xu Y, Meng MQH. An Overview of Systems and Techniques for Autonomous Robotic Ultrasound Acquisitions. IEEE Transactions on Medical Robotics and Bionics 2021; 3: 510–524. https://doi.org/10.1109/TMRB.2021.3072190.
  23. Su K, Liu J, Ren X, et al. A Fully Autonomous Robotic Ultrasound System for Thyroid Scanning. Nature Communications 2024; 15: 4004. https://doi.org/10.1038/s41467-024-48421-y.
  24. Wein W, Brunke S, Khamene A, et al. Automatic CT-Ultrasound Registration for Diagnostic Imaging and Image-Guided Intervention. Medical Image Analysis 2008; 12: 577–585. https://doi.org/10.1016/j.media.2008.06.006.
  25. Shimizu M, Kakuya H, Yoon WK, et al. Analytical Inverse Kinematic Computation for 7-DOF Redundant Manipulators with Joint Limits and Its Application to Redundancy Resolution. IEEE Transactions on Robotics 2008; 24: 1131–1142. https://doi.org/10.1109/TRO.2008.2003266.
  26. Wiedmeyer W, Altoé P, Auberle J, et al. A Real-Time-Capable Closed-Form Multi-Objective Redundancy Resolution Scheme for Seven-DoF Serial Manipulators. IEEE Robotics and Automation Letters 2021; 6: 431–438. https://doi.org/10.1109/LRA.2020.3045646.
  27. Tsai CC, Hung CC, Chang CF. Trajectory Planning and Control of a 7-DOF Robotic Manipulator. In Proceedings of the 2014 International Conference on Advanced Robotics and Intelligent Systems (ARIS), Taipei, Taiwan, 6–8 June 2014; pp. 78–84.

Supporting Agencies

  1. Funding: This research received no external funding.