Abstract

In this work, we present Concentric Tube Robot Design and Path Plan (CTR DaPP), a Python application that utilizes the recently introduced Piecewise Variable Strain approach for the design and path planning of concentric tube robots (CTRs). The application provides a modular platform for implementing and testing combinations of path planners and design optimization algorithms. We apply the Randomly Exploring Rapid Tree and Nelder-Mead algorithms to test the ‘follow-the-leader’ behavior and demonstrate the potential benefits of variable strain tubes in path planning problems. This platform, paired with the variable-strain model, opens up new research avenues to investigate follow-the-leader behavior, elastic stability, and tube design of variable-strain CTRs through simulation.

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