Analyzing Surgeon–Robot Cooperative Performance in Robot-Assisted Intravascular Catheterization
Open accessAbstract
Robot-assisted catheterization offers a promising technique for cardiovascular interventions, addressing the limitations of manual interventional surgery, where precise tool manipulation is critical. In remote-control robotic systems, the lack of force feedback and imprecise navigation challenge cooperation between the surgeon and robot. This study proposes a manipulation-based evaluation framework to assess the cooperative performance between different operators and robot using kinesthetic, kinematic, and haptic data from multi-sensor technologies. The proposed evaluation framework achieves a recognition accuracy of 99.99% in assessing the cooperation between operator and robot. Additionally, the study investigates the impact of delay factors, considering no delay, constant delay, and variable delay, on cooperation characteristics. The findings suggest that variable delay contributes to improved cooperation performance between operator and robot in a primary-secondary isomorphic robotic system, compared to a constant delay factor. Furthermore, operators with experience in manual percutaneous coronary interventions exhibit significantly better cooperative manipulate on with the robot system than those without such experience, with respective synergy ratios of 89.66%, 90.28%, and 91.12% based on the three aspects of delay consideration. Moreover, the study explores interaction information, including distal force of tools-tissue and contact force of hand-control-ring, to unders…
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Reinforcement learning with a 0.1 learning rate stabilizes systems in harvesting and minimizes construction errors, though exploration risks persist.
“Reinforcement learning with a 0.1 learning rate stabilizes systems in harvesting [28] and minimizes construction errors [34], though exploration risks persist [13].”
From Hybrid Fault-Tolerant Control in Cooperative Robotics: Advances in Resilience and Scalability (2025) · cites this paper as [13]
Reinforcement learning exploration risks necessitate extensive validation.
“However, exploration risks necessitate extensive validation [13], a concern given the limited physical testing in the field [3].”
From Hybrid Fault-Tolerant Control in Cooperative Robotics: Advances in Resilience and Scalability (2025) · cites this paper as [13]
Healthcare leverages neural synchronization for reliable surgical outcomes.
“and healthcare leverages neural synchronization for reliable surgical outcomes [11,13].”
From Hybrid Fault-Tolerant Control in Cooperative Robotics: Advances in Resilience and Scalability (2025) · cites this paper as [13]
There is a tension between theoretical potential and practical deployment, particularly in resource-intensive settings like space exploration and high-stakes healthcare.
“These challenges highlight a tension between theoretical potential and practical deployment, particularly in resourceintensive settings like space exploration [6] and high-stakes healthcare [13].”
From Hybrid Fault-Tolerant Control in Cooperative Robotics: Advances in Resilience and Scalability (2025) · cites this paper as [13]
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Yao, T., Lu, B., Kowarschik, M., Yuan, Y., Zhao, H., Sebastien Ourselin, A., Althoefer, K. A., Ge, J., & Qi, P. (2025) · IEEE Reviews in Biomedical Engineering · doi:10.1109/rbme.2025.3641383 · Cited by 30 · see citing works
Hybrid Fault-Tolerant Control in Cooperative Robotics: Advances in Resilience and Scalability
Urrea, C. (2025) · Actuators · doi:10.3390/act14040177 · Cited by 19 · see citing works
“Manufacturing benefits from hybrid systems reducing downtime through parallel kinematics and adaptive control [38,41], and healthcare leverages neural synchronization for reliable surgical outcomes [11,13].”
InfluentialWen, K., & Wu, G. (2025) · Actuators · doi:10.3390/act14050236 · Cited by 3 · see citing works
Mei, Z., Han, S., Qu, S., Wang, N., Xia, Y., Liao, Z., Zhou, W., Jie, C., Guo, S., Wang, H. B., et al. (2026) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2026.3688093
- Authors
- Du, W., Yi, G., Omisore, O. M., Duan, W., Oluwadra Akinyemi, T., Chen, X., Liu, J., Lee, B.-G., & Wang, L.
- Affiliations
- Chinese Academy of Sciences (CN)
- Research Centre for Medical Robotics and Minimally Invasive Surgical Devices, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
- Southern University of Science and Technology (CN)
- University of Nottingham Ningbo China (CN)
- ORCID
- Venue
- IEEE Transactions on Human-Machine Systems
- Published
- 2024-12
- Volume
- 54
- Issue
- 6
- Pages
- 698-710
- ISSN
- 2168-2291 (ISSN-L)
- 2168-2305
- OpenAlex
- W4403123013
- Cited by
- 4
- References
- 0
- Citations by year
- 2026: 1 · 2025: 3
- FWCI
- 0.54
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- IEEE Transactions on Human-Machine Systems (publishedVersion, cc-by)
- Funders
- National Natural Science Foundation of China (U21A20480, U191320006)
- National Key R&D Program of China (2022YFC2409000)
- Shenzhen Science and Technology Program (JSGGKQTD20221101115654021)
- Shenzhen Engineering Laboratory for Diagnosis and Treatment Key Technologies of Interventional Surgical Robots
- Topics
- Physical Sciences › Engineering › Mechanical Engineering › Teleoperation and Haptic Systems
- Health Sciences › Medicine › Surgery › Surgical Simulation and Training
- Physical Sciences › Engineering › Control and Systems Engineering › Robot Manipulation and Learning
- Keywords
- intravascular catheterization, communication delay, operator experience, percutaneous coronary intervention, haptic feedback, contact force, cardiovascular interventions
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