A Hierarchical Framework for Real-Time Path Planning of Microswarm in Dynamic Environments

Abstract

Autonomous navigation of magnetic microswarms in dynamic and unstructured environments is essential for biomedical applications, such as targeted therapy and minimally invasive interventions. However, existing path planning methods struggle to simultaneously achieve real-time adaptability and path smoothness in dynamic obstacle environments. To address this, we propose a hierarchical Dynamic Rapidly-exploring Random Tree Star (D-RRT*) path planning framework that integrates dynamic step size adjustment, local target selection, and local planning that considers microswarms' turning capabilities and energy optimization. Comparative simulations and experiments validate the effectiveness of the proposed planning framework, and results show that it can significantly improve the planning efficiency, path smoothness, and collision avoidance in complex dynamic scenarios.