Learning the Kinematics of Tubular Continuum Robots: Model-based vs. Data-based Methods

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Tubular continuum robots are the smallest among all continuum robots with typical diameter to length ratios of 1:250. The composition of concentrically arranged elastic tubes with pre--curvatures allows for simple, yet dextrous, robotic manipulators at a millimetre scale. Actuation is achieved mechanically by relative rotation and translation of tubes and results in tentacle--like motions. Tubular continuum robots are particularly promising for medical interventions, such as diagnosis through natural orifices or minimally invasive surgery. For instance, they could reach the skull base through the nasal passage to remove tumours or deliver drugs on curvilinear paths to locations deep within the human body. Despite the simple actuation of component tubes, the resulting motion of tubular continuum robots is characterised by a highly non-linear behaviour due to elastic interactions. Today, the modelling, computational design, and motion planning methodologies for those robots are characterised by limitations in terms of accuracy and efficiency. The gold standard approaches are a trade-off between the consideration of a limited number of physical parameters and computational expense. As a result, tubular continuum robots have not left the laboratory bench-top despite all their potential merits in health care. This research programme aims in leveraging data-based approaches and deep learning techniques for modelling, computational design, and motion planning. The proposed research is structured around four scientific key questions: 1) How can the curvilinear structure, the morphological constraints, and the mechanical laws that govern tubular continuum robots in the real world be leveraged and exploited by deep learning? 2) How can data be generated, experimentally obtained, and represented for tubular continuum robots in order to inform learning-based approaches? 3) How can learning-based approaches help to exploit the relevant parameter space for physics-based models of tubular continuum robots? 4) How great is the potential to overcome the limitations of current model-based approaches with data-driven problem solving? To the best of my knowledge, learning-based approaches are proposed for the first time for tubular continuum robots by my research group. Deep learning can serve to discover unknown problem structures and to derive novel knowledge, which can then be used to improve and expand existing problem-specific algorithms. The proposed programme will generate scientific methodologies for tubular continuum robots in particular as well as soft and continuum robotics in general. It will further contribute to the understanding of relevant physical phenomena for model-based solutions. The expected step change research results will ultimately serve as an enabler to exploit the full potential of tubular continuum robots in medical interventions.