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Nanobots have attracted significant attention for precision medicine due to their potential to improve the precision and effectiveness of medical treatments. One of the major challenges in intravascular nanobot applications is planning a safe and efficient navigation path within the highly constrained environment of blood vessels. This paper presents a preliminary study on dynamic path planning for nanobot navigation in blood vessels. The proposed framework considers vessel geometry and obstacle avoidance to generate feasible navigation paths. A distance-transform-based path planning approach is employed as the initial navigation strategy and evaluated through simulations on simplified vascular models. The study investigates the ability of the planner to adapt to dynamic environments while maintaining an efficient route toward a designated target. The final navigation path is generated using a linear combination strategy incorporating a caution factor to improve navigation safety near obstacles. Performance is evaluated in terms of path length and navigation feasibility under various obstacle configurations. Preliminary simulation results demonstrate that the proposed framework can generate adaptive navigation paths, highlighting its potential for future nanomedical applications. This work serves as an initial step toward the development of intelligent navigation systems for intravascular nanobots. Future work will incorporate realistic hemodynamic models, vessel elasticity, and multi-objective optimization to further enhance performance and robustness of nanobots navigation in complex vascular environments.
