Genetic Algorithm to Optimize Design of Micro-Surgical Scissors
<|reference_start|>Genetic Algorithm to Optimize Design of Micro-Surgical Scissors: Microrobotics is an attractive area of research as small-scale robots have the potential to improve the precision and dexterity offered by minimally invasive surgeries. One example of such a tool is a pair of micro-surgical scissors that was developed for cutting of tumors or cancerous tissues present deep inside the body such as in the brain. This task is often deemed difficult or impossible with conventional robotic tools due to their size and dexterity. The scissors are designed with two magnets placed a specific distance apart to maximize deflection and generate cutting forces. However, remote actuation and size requirements of the micro-surgical scissors limits the force that can be generated to puncture the tissue. To address the limitation of small output forces, we use an evolutionary algorithm to further optimize the performance of the scissors. In this study, the design of the previously developed untethered micro-surgical scissors has been modified and their performance is enhanced by determining the optimal position of the magnets as well as the direction of each magnetic moment. The developed algorithm is successfully applied to a 4-magnet configuration which results in increased net torque. This improvement in net torque is directly translated into higher cutting forces. The new configuration generates a cutting force of 58 mN from 80 generations of the evolutionary algorithm which is a 1.65 times improvement from the original design. Furthermore, the developed algorithm has the advantage that it can be deployed with minor modifications to other microrobotic tools and systems, opening up new possibilities for various medical procedures and applications.<|reference_end|>
@article{norouziani2024genetic,
    title={Genetic Algorithm to Optimize Design of Micro-Surgical Scissors},
    author={Fatemeh Norouziani, Veerash Palanichamy, Shivam Gupta and Onaizah
  Onaizah},
    journal={arXiv preprint arXiv:2407.15243},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.15243},
    primaryClass={cs.RO cs.AI}
}