Document Type
Article
Publication Date
2026
DOI
10.1155/joro/6114590
Publication Title
Journal of Robotics
Volume
2026
Issue
1
Pages
6114590 (23 pp.)
Abstract
We present a safe and efficient path planning framework for nanobot navigation in complex vascular networks, enabling precise drug delivery to hard-to-reach tumor regions such as those found in the lungs. The proposed method introduces a safe and efficient path planning strategy that integrates a utility-based function to balance two key goals: exploring uncertain areas to gather valuable information and avoiding hazardous regions to ensure safe and reliable delivery. We evaluate a simplified swarm-based nanobot navigation scenario using synthetic vascular maps that mimic the structural complexity of lung tumor blood vessels. Environmental uncertainty is modeled from vessel topology, blockage density, and flow dynamics to simulate real biological conditions. Each navigation trial is evaluated based on how well the nanobot avoids risky regions, reduces environmental uncertainty, and reaches the target efficiently. Our methodology builds on established literature in information-theoretic (IT) planning and biomedical robotics. Compared with the classical rapidly exploring random tree star (RRT∗) baseline, the proposed RRT∗-IT approach achieved 79% greater information gain (IG, 8.06 vs. 4.50, computed via Bayesian entropy updating) while reducing the navigational risk (NR) rate from 40.0% to 22.9%, resulting in safer trajectories in high-complexity vascular networks. Planning times averaging 1.05 ± 0.18 s per run, confirming computational feasibility for offline preoperative planning in biomedical applications. The modular design is scalable and ready to integrate future extensions, such as real-time sensor feedback, digital twin models, and patient-specific anatomy. This work contributes a validated planning algorithm that improves delivery safety and reliability by guiding nanobots away from high-risk zones and toward clinically relevant targets. It serves as a validated computational baseline for future development of intelligent, uncertainty-aware microrobotic platforms.
Rights
Copyright © 2026 Nagesh Bheesetty et al.
This is an open access article under the terms of the Creative Commons Attribution 4.0 International (CC BY 4.0) License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
Data Availability
Article states: "The data that support the findings of this study are available in the Supporting Information of this article. The data that the authors used are synthetic data to test the algorithms on their local computers."
ORCID
0009-0009-1889-6427 (Bheesetty), 0000-0002-1490-5404 (Park), 0000-0003-2824-4528 (Alla), 0009-0005-0567-8042 (Nimmagadda), 0000-0001-7860-9535 (Samandari), 0000-0001-5588-6329 (Lee)
Original Publication Citation
Bheesetty, N., Park, J. H., Alla, S., Lo, W. M., Nimmagadda, A., Samandari, M., & Lee, J. H. (2026). Information-theoretic path planning for safe and adaptive nanobot drug delivery. Journal of Robotics, 2026(1), Article 6114590. https://doi.org/10.1155/joro/6114590
Repository Citation
Bheesetty, Nagesh; Park, John H.; Alla, Sujatha; Lo, Winifred M.; Nimmagadda, Aditya; Samandari, Mohamadmahdi; and Lee, Jae H., "Information-Theoretic Path Planning for Safe and Adaptive Nanobot Drug Delivery" (2026). Engineering Management & Systems Engineering Faculty Publications. 283.
https://digitalcommons.odu.edu/emse_fac_pubs/283
Supporting Information 1
Included in
Nanomedicine Commons, Nanoscience and Nanotechnology Commons, Operations Research, Systems Engineering and Industrial Engineering Commons, Theory and Algorithms Commons