Trajectory Optimization for Tooth Preparation Robot Based on P-MRSD Algorithm

Oral diseases, including dental caries and cracked teeth, are leading causes of tooth loss and can pose significant health risks if left untreated. Manual tooth preparation by dentists is often prone to visual bias and positioning errors. To address these issues, tooth preparation robots, driven by automation and intelligence, have been proposed as replacements for repetitive tasks. However, existing tooth preparation robots with serial systems suffer from low preparation accuracy and poor safety when preparing real teeth of hard and brittle materials. This includes the problems of low accuracy of theoretical preparation trajectory planning and deformation of the end of the serial system due to force. In this study, we propose a preparation trajectory optimization strategy that includes methods for surface morphology optimization and end deformation optimization to enhance preparation accuracy and safety. Our approach utilizes the proposed prediction of material residue and stiffness deformation (P-MRSD) method to optimize tooth morphology based on five key parameters, such as the bur pose and preparation trajectory. Additionally, the extrusion force during preparation is optimized by considering factors such as the material removal rate, the Euclidean distance field of tool contacts (TC), and the system stiffness of the tooth preparation robot. The accuracy and safety of the execution trajectory are ensured by minimizing stiffness deformation. Finally, a tooth preparation robot hardware system is developed to verify the correlation between predicted and observed tooth morphology, with end deformation optimization based on the optimized trajectory. The feasibility of the robot for preparing hard and brittle teeth is demonstrated, and the proposed trajectory optimization method improves both accuracy and safety in preparation process. This provides a theoretical foundation and technical support for advancing automated robotic technology, particularly in the development of more accurate and safer serial robotic arm systems. Note to Practitioners—The motivation of this paper is to address the challenge of trajectory optimization for grinding hard and brittle materials using a serial robotic system, with potential applications in machining. The interaction between tool pose, trajectory, and material (specifically a cracked hard and brittle tooth) is analyzed to investigate its impact on preparation. Two optimization parameters are proposed to refine the theoretical preparation trajectory based on the surface morphology indexes. Real preparation experiments are then conducted using the proposed extrusion force optimization model, which enhances the motion accuracy of the theoretical trajectory. This improvement boosts the performance of the serial robotic arm system when preparing hard and brittle materials. The findings also suggest that computer-aided design and manufacturing systems could autonomously generate preparation trajectory optimization plans that consider both surface morphology and safety, offering insights and support for machining in other fields. Preliminary physical experiments demonstrate the feasibility of the proposed trajectory planning and kinematic parameter optimization methods, improving both preparation accuracy and safety. However, the research is still in the laboratory phase and has not yet been applied clinically. Future studies will focus on balancing safety and accuracy when the serial system operates intraorally, advancing the potential clinical application of tooth preparation robots.

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Publication Details

Journal
IEEE Transactions on Automation Science and Engineering
Published
2025-01-01
DOI
https://doi.org/10.1109/tase.2025.3555227
Citations
2
Primary Topic
Robotic Path Planning Algorithms
Type
article
Field-Weighted Citation Impact
2.55

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article

Trajectory Optimization for Tooth Preparation Robot Based on P-MRSD Algorithm

Jingang Jiang, Jie Pan, Jianpeng Sun, Ao Li et al.
2 citations
IEEE Transactions on Automation Science and Engineering
Robotic Path Planning Algorithms
2.55
article

Trajectory Optimization for Tooth Preparation Robot Based on P-MRSD Algorithm

Jingang Jiang, Jie Pan, Jianpeng Sun, Ao Li, Zhonghao Xue, Chunrui Wang
article en
2 citations

Abstract

Oral diseases, including dental caries and cracked teeth, are leading causes of tooth loss and can pose significant health risks if left untreated. Manual tooth preparation by dentists is often prone to visual bias and positioning errors. To address these issues, tooth preparation robots, driven by automation and intelligence, have been proposed as replacements for repetitive tasks. However, existing tooth preparation robots with serial systems suffer from low preparation accuracy and poor safety when preparing real teeth of hard and brittle materials. This includes the problems of low accuracy of theoretical preparation trajectory planning and deformation of the end of the serial system due to force. In this study, we propose a preparation trajectory optimization strategy that includes methods for surface morphology optimization and end deformation optimization to enhance preparation accuracy and safety. Our approach utilizes the proposed prediction of material residue and stiffness deformation (P-MRSD) method to optimize tooth morphology based on five key parameters, such as the bur pose and preparation trajectory. Additionally, the extrusion force during preparation is optimized by considering factors such as the material removal rate, the Euclidean distance field of tool contacts (TC), and the system stiffness of the tooth preparation robot. The accuracy and safety of the execution trajectory are ensured by minimizing stiffness deformation. Finally, a tooth preparation robot hardware system is developed to verify the correlation between predicted and observed tooth morphology, with end deformation optimization based on the optimized trajectory. The feasibility of the robot for preparing hard and brittle teeth is demonstrated, and the proposed trajectory optimization method improves both accuracy and safety in preparation process. This provides a theoretical foundation and technical support for advancing automated robotic technology, particularly in the development of more accurate and safer serial robotic arm systems. Note to Practitioners—The motivation of this paper is to address the challenge of trajectory optimization for grinding hard and brittle materials using a serial robotic system, with potential applications in machining. The interaction between tool pose, trajectory, and material (specifically a cracked hard and brittle tooth) is analyzed to investigate its impact on preparation. Two optimization parameters are proposed to refine the theoretical preparation trajectory based on the surface morphology indexes. Real preparation experiments are then conducted using the proposed extrusion force optimization model, which enhances the motion accuracy of the theoretical trajectory. This improvement boosts the performance of the serial robotic arm system when preparing hard and brittle materials. The findings also suggest that computer-aided design and manufacturing systems could autonomously generate preparation trajectory optimization plans that consider both surface morphology and safety, offering insights and support for machining in other fields. Preliminary physical experiments demonstrate the feasibility of the proposed trajectory planning and kinematic parameter optimization methods, improving both preparation accuracy and safety. However, the research is still in the laboratory phase and has not yet been applied clinically. Future studies will focus on balancing safety and accuracy when the serial system operates intraorally, advancing the potential clinical application of tooth preparation robots.

IEEE Transactions on Automation Science and EngineeringVol. 22
Harbin University of Science and Technology (CN), Peking University (CN)
Natural Science Foundation of Heilongjiang Province
Zero hunger
Openalex Percentile: Top 12%
Robotic Path Planning Algorithms
2.55
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