Potential field-based approaches for nanobotics in drug delivery

Abstract

Nanorobotics has transformed targeted drug delivery by enhancing therapeutic efficacy, minimizing off-target effects, and increasing precision. However, navigating complex biological environments is challenging. In the field of macroscopic robotics, potential field (PF)-based approaches that utilize attractive and repulsive virtual forces provide a promising framework that can be applied to path planning for nanorobots. This study modifies PF algorithms for nanorobotic navigation to address challenges such as avoiding dynamic obstacles, escaping local minima, and optimizing trajectories in real time. We evaluated the movement of the nanorobot through simulations under static and dynamic conditions for the targets and obstacles. The results demonstrate that nanorobotics with hybrid PF methodologies enhance navigation performance, enabling nanorobots to successfully navigate through biological barriers and efficiently reach their target locations. This work is a significant step towards intelligent and autonomous nanorobotic drug delivery systems and contributes to practical biomedical applications.