A CT Image-Based Virtual Sensing Method to Estimate Bone Drilling Force for Surgical Robots

Li, Liang; Yang, Sheng; Peng, Wuke; Wang, Guangzhi

doi:10.1109/tbme.2021.3108400

Cited by 5 publications

(4 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The stiffness of a robot mainly is mainly contributed by the links and the joints. Due to the fact that the deformation at joints is much larger than the deformation at links, it is assumed that the links are rigid and only the joints are deformed, as shown in [ 14 ]. Therefore, the robot stiffness can be evaluated from the joint stiffness as follows.…”

Section: Methodsmentioning

confidence: 99%

“…Pourkand et al utilized augmented haptic feedback to simulate the anatomic variability in bone by generating clinically relevant force levels during orthopedic drilling through a predictive model informed by experimental data, achieving accurate force prediction and practical application using a customized Phantom Premium haptic device [ 13 ]. Li et al introduced the gray value of a CT image of bone tissue as a weight into the mechanical model of bone drilling and verified the accuracy of the mechanical model through experiments [ 14 ].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Force-Position Hybrid Compensation Control for Path Deviation in Robot-Assisted Bone Drilling

Li,

Zhong,

Yang

et al. 2023

Sensors

View full text Add to dashboard Cite

Bone drilling is a common procedure in orthopedic surgery and is frequently attempted using robot-assisted techniques. However, drilling on rigid, slippery, and steep cortical surfaces, which are frequently encountered in robot-assisted operations due to limited workspace, can lead to tool path deviation. Path deviation can have significant impacts on positioning accuracy, hole quality, and surgical safety. In this paper, we consider the deformation of the tool and the robot as the main factors contributing to path deviation. To address this issue, we establish a multi-stage mechanistic model of tool–bone interaction and develop a stiffness model of the robot. Additionally, a joint stiffness identification method is proposed. To compensate for path deviation in robot-assisted bone drilling, a force-position hybrid compensation control framework is proposed based on the derived models and a compensation strategy of path prediction. Our experimental results validate the effectiveness of the proposed compensation control method. Specifically, the path deviation is significantly reduced by 56.6%, the force of the tool is reduced by 38.5%, and the hole quality is substantially improved. The proposed compensation control method based on a multi-stage mechanistic model and joint stiffness identification method can significantly improve the accuracy and safety of robot-assisted bone drilling.

show abstract

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Force-Position Hybrid Compensation Control for Path Deviation in Robot-Assisted Bone Drilling

Li,

Zhong,

Yang

et al. 2023

Sensors

View full text Add to dashboard Cite

show abstract

“…Therefore, SRS cannot interpret preoperative images in the same way as surgeons, hindering their ability to optimally utilize preoperative data for the stable and efficient execution of surgical operations. [67] Complex signal processing is used for intraoperative monitoring and judgment. Data from the intraoperative sensor feedback were monitored by setting safety thresholds or through manual observation to ensure surgical safety.…”

Section: Perception and Navigationmentioning

confidence: 99%

Section: Characteristics Of Srsmentioning

confidence: 99%

Evolution of Surgical Robot Systems Enhanced by Artificial Intelligence: A Review

Liu,

Wu,

Sang

et al. 2024

Advanced Intelligent Systems

View full text Add to dashboard Cite

Surgical robot systems (SRS) represent an innovative cross‐disciplinary research field using robotic technology to assist surgeons in operations. Current bottlenecks in SRS, such as the limited ability to process complex information and make surgical decisions, have not been effectively solved. Artificial intelligence (AI) is a valuable technique for simulating and extending human intelligence. AI offers a new direction and impetus for SRS by enhancing performance in areas such as perception, navigation, surgical planning, and control strategies. This review introduces the developmental history of AI‐aided SRS, summarizes the basic SRS architecture, and analyzes how AI can improve SRS performance. Classical cases of AI‐aided SRS, the impact of evidence in clinical settings, and associated ethical and legal considerations are explored. Finally, the challenges in AI‐aided SRS are discussed, including algorithm development, surgical data science, human–robot coordination, and trust building between humans and robots.

show abstract

Leveraging ultrasonic actuation during inclined orthopaedic bone drilling: An experimental and histological study

2023

View full text Add to dashboard Cite

A CT Image-Based Virtual Sensing Method to Estimate Bone Drilling Force for Surgical Robots

Cited by 5 publications

References 28 publications

Force-Position Hybrid Compensation Control for Path Deviation in Robot-Assisted Bone Drilling

Force-Position Hybrid Compensation Control for Path Deviation in Robot-Assisted Bone Drilling

Evolution of Surgical Robot Systems Enhanced by Artificial Intelligence: A Review

Leveraging ultrasonic actuation during inclined orthopaedic bone drilling: An experimental and histological study

Contact Info

Product

Resources

About