Magnetic soft continuum robots(MSCRs)offer transformative potential for minimally invasive procedures due to their high flexibility and magnetic responsiveness.However,reliable and efficient programming of MSCRs for a...Magnetic soft continuum robots(MSCRs)offer transformative potential for minimally invasive procedures due to their high flexibility and magnetic responsiveness.However,reliable and efficient programming of MSCRs for anatomical adaptability and precise tip manipulation remains a key challenge,particularly in navigating tortuous pathways and targeting hard-to-reach lesions.Addressing this,we propose a unified inverse programming framework based on Physics-Informed Neural Networks(PINNs)that simultaneously tackles two critical design objectives in MSCR applications:shape morphing and tip trajectory control.The shape morphing problem involves programming magnetization distributions during fabrication to achieve desired global geometries,while trajectory control is realized by designing time-varying magnetic fields to guide the robot tip along prescribed paths.Leveraging the hard-magnetic elastica model,we reformulate the inverse design challenge into solving a nonlinear ordinary differential equation(ODE).The proposed PINN-based framework seamlessly integrates physical priors into the learning process,enabling rapid convergence while requiring only sparse data.We validate our approach using complex geometries,including shapes resembling the letters“USTC”,and benchmark the results against finite difference(FDM)and finite element method(FEM)simulations.The strong agreement across methods confirms the reliability and accuracy of the PINN-based framework.Our method offers a versatile and computationally efficient tool for the inverse design and control of programmable MSCRs and opens new pathways for data-free,high-fidelity,multi-objective optimization in magnetically actuated soft robotics.展开更多
We survey fundamental concepts for inverse programming and then present the Universal Resolving Algorithm, an algorithm for inverse computation in a first order, functional programming language. We discuss the key co...We survey fundamental concepts for inverse programming and then present the Universal Resolving Algorithm, an algorithm for inverse computation in a first order, functional programming language. We discuss the key concepts of the algorithm, including a three step approach based on the notion of a perfect process tree, and demonstrate our implementation with several examples of inverse computation.展开更多
基金supported by the National Key Research and Development Program of China(Grant No.2024YFE0215200)the National Natural Science Foundation of China(Grant No.12272369).
文摘Magnetic soft continuum robots(MSCRs)offer transformative potential for minimally invasive procedures due to their high flexibility and magnetic responsiveness.However,reliable and efficient programming of MSCRs for anatomical adaptability and precise tip manipulation remains a key challenge,particularly in navigating tortuous pathways and targeting hard-to-reach lesions.Addressing this,we propose a unified inverse programming framework based on Physics-Informed Neural Networks(PINNs)that simultaneously tackles two critical design objectives in MSCR applications:shape morphing and tip trajectory control.The shape morphing problem involves programming magnetization distributions during fabrication to achieve desired global geometries,while trajectory control is realized by designing time-varying magnetic fields to guide the robot tip along prescribed paths.Leveraging the hard-magnetic elastica model,we reformulate the inverse design challenge into solving a nonlinear ordinary differential equation(ODE).The proposed PINN-based framework seamlessly integrates physical priors into the learning process,enabling rapid convergence while requiring only sparse data.We validate our approach using complex geometries,including shapes resembling the letters“USTC”,and benchmark the results against finite difference(FDM)and finite element method(FEM)simulations.The strong agreement across methods confirms the reliability and accuracy of the PINN-based framework.Our method offers a versatile and computationally efficient tool for the inverse design and control of programmable MSCRs and opens new pathways for data-free,high-fidelity,multi-objective optimization in magnetically actuated soft robotics.
文摘We survey fundamental concepts for inverse programming and then present the Universal Resolving Algorithm, an algorithm for inverse computation in a first order, functional programming language. We discuss the key concepts of the algorithm, including a three step approach based on the notion of a perfect process tree, and demonstrate our implementation with several examples of inverse computation.
