A Hierarchical Framework for Real-Time Path Planning of Microswarm in Dynamic Environments
Yamei Li, Ruijian Ge, Aoji Zhu, Jiachi Zhao, Danjing Shi, Yinghan Sun, Yangmin Li, Lidong Yang
AI summary
Problem
Existing path planning methods struggle to simultaneously achieve real-time adaptability and path smoothness for magnetic microswarms navigating complex, dynamic obstacle environments.
Approach
A hierarchical D-RRT* framework that dynamically adjusts step sizes based on local obstacle density and distribution, autonomously selects intermediate targets, and incorporates turning constraints into local path planning.
Key results
- Dynamic step size adjustment adapts to obstacle density and distribution
- Autonomous local target selection ensures path smoothness and continuity
- Significantly improves planning efficiency, path smoothness, and collision avoidance
Why it matters
Enables reliable, autonomous navigation of magnetic microswarms for critical biomedical applications like targeted therapy and minimally invasive interventions.
Abstract
Autonomous navigation of magnetic microswarms in dynamic and unstructured environments is essential for biomedi- cal applications, such as targeted therapy and minimally invasive interventions. However, existing path planning methods strug- gle 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.