Evolutionary design of magnetic soft continuum robots

Abstract

Significance Magnetic soft continuum robots (MSCRs) capable of magnetic field–controllable steering offer great promise for the endovascular treatment of cardiovascular diseases. However, existing MSCRs are often limited to small workspaces because of the lack of efficient design and optimization tools. In this work, we introduce an evolutionary design strategy by integrating theoretical modeling and the genetic algorithm. Enabled by this strategy, the proposed MSCR achieves an optimal workspace much larger than those of the state-of-the-art MSCRs. The proposed design strategy also offers a potent tool for efficient design and optimization of future magnetic soft robots and actuators. Worldwide cardiovascular diseases such as stroke and heart disease are the leading cause of mortality. While guidewire/catheter-based minimally invasive surgery is used to treat a variety of cardiovascular disorders, existing passive guidewires and catheters suffer from several limitations such as low steerability and vessel access through complex geometry of vasculatures and imaging-related accumulation of radiation to both patients and operating surgeons. To address these limitations, magnetic soft continuum robots (MSCRs) in the form of magnetic field–controllable elastomeric fibers have recently demonstrated enhanced steerability under remotely applied magnetic fields. While the steerability of an MSCR largely relies on its workspace—the set of attainable points by its end effector—existing MSCRs based on embedding permanent magnets or uniformly dispersing magnetic particles in polymer matrices still cannot give optimal workspaces. The design and optimization of MSCRs have been challenging because of the lack of efficient tools. Here, we report a systematic set of model-based evolutionary design, fabrication, and experimental validation of an MSCR with a counterintuitive nonuniform distribution of magnetic particles to achieve an unprecedented workspace. The proposed MSCR design is enabled by integrating a theoretical model and the genetic algorithm. The current work not only achieves the optimal workspace for MSCRs but also provides a powerful tool for the efficient design and optimization of future magnetic soft robots and actuators.