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Article Navigation >  IEEE/CAA Journal of Automatica Sinica  > 2026  > In Press, Accepted Manuscript
W. Song, A. Ramos–Sebastian, and S. H. Kim, “Dynamic centerline path–planning for the autonomous locomotion of magnetic milli/microrobots using magnetic trap-based navigation systems,” IEEE/CAA J. Autom. Sinica, early access, 2026. doi: 10.1109/JAS.2026.126533
Citation: W. Song, A. Ramos–Sebastian, and S. H. Kim, “Dynamic centerline path–planning for the autonomous locomotion of magnetic milli/microrobots using magnetic trap-based navigation systems,” IEEE/CAA J. Autom. Sinica, early access, 2026. doi: 10.1109/JAS.2026.126533
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Dynamic Centerline Path–Planning for the Autonomous Locomotion of Magnetic Milli/Microrobots Using Magnetic Trap-Based Navigation Systems

doi: 10.1109/JAS.2026.126533
Funds:  This paragraph of the first footnote will contain the date on which you submitted your paper for review. This work was supported by a Korea University grant and by the National Research Foundation of Korea (NRF) funded by the Korea government (MSIT) (2022R1A2C1003381)
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    Abstract
  • Magnetic microrobots have shown strong potential for targeted therapies in vascular environments; however, complex vessel geometries, flow disturbances, and limited imaging feedback often require manual operation, increasing the risk of vessel damage. Moreover, the lack of high-rate 3D tracking hinders the implementation of fully autonomous control systems. To address these challenges, we propose a magnetic trap-based 2D autopilot framework for real-time autonomous navigation of magnetic robots under limited-feedback conditions. The framework integrates a Trapping Point Error Compensation (TPEC) strategy, which improves open-loop control accuracy by empirically mapping desired trajectory points to experimentally reachable robot states and their corresponding control inputs, thereby compensating for system imperfections without position feedback. It also incorporates the Skeletonization and Quasi-Euclidean Path-Planning (SQEP) algorithm, which performs clearance-aware path-planning on a skeletonized channel representation to generate safe, executable trajectories while accounting for the robot’s physical dimensions. Experimental results show that TPEC achieves locomotion speeds approximately four times higher than those of a conventional PID controller while maintaining comparable accuracy, substantially reducing dependence on high-rate feedback. Comparative evaluations demonstrate that SQEP consistently generates clearance-aware trajectories across representative channel geometries, achieving an average centerline-adherence accuracy of 96.8%, mean deviations below one pixel (~67 µm), and computation times under 300 ms. The integrated framework enables robust autonomous navigation through confined channels with abrupt geometric variations and fluid flow disturbances. These results demonstrate the potential of the proposed methodology for autonomous navigation in controlled 2D confined environments and provide a basis for future extensions toward vascular applications.

     

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