Abstract
Introduction: Compliant mechanisms, especially continuum robots, are becoming integral to advancements in minimally invasive surgery due to their ability to autonomously navigate natural pathways, significantly reducing collision severity. A major challenge lies in developing an effective control strategy to accurately reflect their behavior for enhanced operational precision.
Methods: This study examines the trajectory tracking capabilities of a tendon-driven continuum robot at its tip. We introduce a novel feedforward control methodology that leverages a mathematical model based on Cosserat rod theory. To mitigate the computational challenges inherent in such models, we implement an implicit time discretization strategy. This approach simplifies the governing equations into space-domain ordinary differential equations, facilitating real-time computational efficiency. The control strategy is devised to enable the robot tip to follow a dynamically prescribed trajectory in two dimensions.
Results: The efficacy of the proposed control method was validated through experimental tests on six different demand trajectories, with a motion capture system employed to assess positional accuracy. The findings indicate that the robot can track trajectories with an accuracy within 9.5%, showcasing consistent repeatability across different runs.
Discussion: The results from this study mark a significant step towards establishing an efficient and precise control methodology for compliant continuum robots. The demonstrated accuracy and repeatability of the control approach significantly enhance the potential of these robots in minimally invasive surgical applications, paving the way for further research and development in this field.
Methods: This study examines the trajectory tracking capabilities of a tendon-driven continuum robot at its tip. We introduce a novel feedforward control methodology that leverages a mathematical model based on Cosserat rod theory. To mitigate the computational challenges inherent in such models, we implement an implicit time discretization strategy. This approach simplifies the governing equations into space-domain ordinary differential equations, facilitating real-time computational efficiency. The control strategy is devised to enable the robot tip to follow a dynamically prescribed trajectory in two dimensions.
Results: The efficacy of the proposed control method was validated through experimental tests on six different demand trajectories, with a motion capture system employed to assess positional accuracy. The findings indicate that the robot can track trajectories with an accuracy within 9.5%, showcasing consistent repeatability across different runs.
Discussion: The results from this study mark a significant step towards establishing an efficient and precise control methodology for compliant continuum robots. The demonstrated accuracy and repeatability of the control approach significantly enhance the potential of these robots in minimally invasive surgical applications, paving the way for further research and development in this field.
Original language | English |
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Article number | 1358857 |
Journal | Frontiers in Robotics and AI |
Volume | 11 |
Early online date | 16 Apr 2024 |
DOIs | |
Publication status | Published - 16 Apr 2024 |
Data Availability Statement
The raw data supporting the conclusion of this article will bemade available by the authors, without undue reservation.
Funding
The author(s) declare that financial support was received for the research, authorship, and/or publication of this article. This work was funded by a small internal experimental grant from the University of Bath.
Funders | Funder number |
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University of Bath |
Keywords
- compliant robot
- continuum robot
- robot control
- robot modeling
- trajectory tracking
ASJC Scopus subject areas
- Artificial Intelligence
- Computer Science Applications
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Dataset for paper "Model-based Trajectory Tracking of a Compliant Continuum Robot"
Georgilas, I. (Creator), Bailey, N. (Creator), Pekris, S. (Creator) & Atkins, T. (Creator), University of Bath, 16 Apr 2024
DOI: 10.15125/BATH-01392
Dataset