Learning-based intelligent trajectory planning for auto navigation of magnetic robots.

ABSTRACT: Introduction: Electromagnetically controlled small-scale robots show great potential in precise diagnosis, targeted delivery, and minimally invasive surgery. The automatic navigation of such robots could reduce human intervention, as well as the risk and difficulty of surgery. However, it is challenging to build a precise kinematics model for automatic robotic control because the controlling process is affected by various delays and complex environments. Method: Here, we propose a learning-based intelligent trajectory planning strategy for automatic navigation of magnetic robots without kinematics modeling. The Long Short-Term Memory (LSTM) neural network is employed to establish a global mapping relationship between the current sequence in the electromagnetic actuation syste

SUBMITTER: Kou Y 

PROVIDER: S-EPMC10750377 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature