Feasibility of Fiber Reinforcement Within Magnetically Actuated Soft Continuum Robots

Abstract

Soft continuum manipulators have the potential to replace traditional surgical catheters; offering greater dexterity with access to previously unfeasible locations for a wide range of interventions including neurological and cardiovascular. Magnetically actuated catheters are of particular interest due to their potential for miniaturization and remote control. Challenges around the operation of these catheters exist however, and one of these occurs when the angle between the actuating field and the local magnetization vector of the catheter exceeds 90°. In this arrangement, deformation generated by the resultant magnetic moment acts to increase magnetic torque, leading to potential instability. This phenomenon can cause unpredictable responses to actuation, particularly for soft, flexible materials. When coupled with the inherent challenges of sensing and localization inside living tissue, this behavior represents a barrier to progress. In this feasibility study we propose and investigate the use of helical fiber reinforcement within magnetically actuated soft continuum manipulators. Using numerical simulation to explore the design space, we optimize fiber parameters to enhance the ratio of torsional to bending stiffness. Through bespoke fabrication of an optimized helix design we validate a single, prototypical two-segment, 40 mm × 6 mm continuum manipulator demonstrating a reduction of 67% in unwanted twisting under actuation.

Magnetic Soft Continuum Robots With Braided Reinforcement

“…We demonstrate the practical advantage of this approach through autonomous follow-theleader navigation into the pancreatic and bile ducts of a planar soft gelatinous phantom. This represents a continuation of our earlier work ( [18]), but the proposed MCR design is 3 times smaller and exhibits a far higher ratio of twisting to bending stiffness. These improvements result in the first demonstration of such large deformations being generated during the insertion process without the requirement of forces of anatomical interaction.…”

“…Pure bending and pure twisting characterization experiments were therefore performed, as illustrated in Figure 3. Samples were prepared in accordance with the method introduced in Section II-B with radial magnetization for the twisting samples and axial magnetization for the bending samples [18]. Three identical versions of each sample were hung vertically and exposed to an orthogonal 1-D field ramped from -25 mT to 25 mT for three repetitions each.…”

“…However, a fundamental limitation of this non-axial magnetization design arises when a shape-forming MCR attempts to navigate a path featuring large (90 • degrees or more) deformation. As described in detail in [18], in order to generate large deformations, the applied magnetic field must oppose the MCR's magnetization generating an inverted pendulum instability. If unconstrained, in accordance with Timoshenko beam theory, the much lower energy pose available via twisting around the catheter's long 'easy' axis will result (Figure 1b and supporting video).…”

“…Closed loop control such as [19] could theoretically counteract this instability however this would require accurate, high frequency feedback from inside the body of both bending and twisting deformations, which currently represents a major challenge. Mechanical reinforcement is an alternative and more practical approach and was introduced to a large diameter (6 mm) MCR design in the form of double helix fibers [18]. This approach demonstrated significant improvement but proved difficult to miniaturize due to its complex fabrication process.…”

Evolutionary Inverse Material Identification: Bespoke Characterization of Soft Materials Using a Metaheuristic Algorithm

“…Finally, for the Type 4 geometry, the fitting error of the model parameters is larger. The errors may be caused by different factors, such as in segmentation of the experimental image, or an undesired torsion of the sample as observed during experimental testing (see Sample Type 4 in Figure 8A) (Lloyd et al, 2021) which is not captured within the 2D simulation.…”