Key result
Data-driven approach matches optimization-based precision with ~100x faster convergence for concentric tube robots.
Why the study?
Conventional force sensors are not feasible inside the human body for concentric tube continuum robots, and existing estimation methods often suffer from slow convergence.
A novel data-driven approach for estimating contact forces in concentric tube continuum robots achieves comparable precision to optimization methods but is nearly 100 times faster.
May enable real-time force estimation in robotic procedures; leaves open clinical validation.
Concentric tube continuum robots (CTCRs) belong to the family of continuum robots with applications in minimally invasive surgeries. Because of this application domain, measuring the external forces along the body of the robot is paramount. CTCRs are made up of thin elastic rods and are intended to be applied inside the human body, where conventional sensor-based measurements are not feasible. Consequently, research is resorting to estimate the forces through geometric, numeric, or optimization methods. However, these methods often suffer from slow convergence. In this paper, we introduce a novel data-driven approach for estimating contact forces along the body of a CTCR that offers an estimation precision comparable to the current state-of-the-art optimization-based approaches, but exhibits nearly two orders of magnitude faster convergence. The proposed method is scalable and exhibits a significant performance in response to a wide range of external forces. The approach was evaluated in simulations and on a real 2-tube CTCR.
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Donat et al. (2023) studied Minimally invasive surgeries. Data-driven approach for estimating contact forces vs. State-of-the-art optimization-based approaches was evaluated on Estimation precision and convergence speed. A novel data-driven approach for estimating contact forces along a concentric tube continuum robot offered precision comparable to optimization-based approaches but with nearly two orders of magnitude faster convergence.
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