Copyright © 2026 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

@article{191833,
        author = {Anshum Rankawat and Aman Shakil Shaikh},
        title = {Flight Path Planning for UAVs Using Machine Learning-Guided RRT* Algorithms},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {12},
        number = {no},
        pages = {176-182},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=191833},
        abstract = {Unmanned Aerial Vehicles (UAVs) require reliable, adaptive, and computationally efficient flight path planning to ensure safe navigation in both static and dynamic environments. This study introduces a unified framework that integrates Guided RRT* and Enhanced Guided RRT* algorithms with a Machine Learning-based reliability estimation layer. The framework is designed to generate smoother and collision-free trajectories while dynamically responding to environmental changes. A predictive reliability model using supervised learning adjusts the path expansion process based on safety confidence, enabling the UAV to maintain behavioural stability under uncertainty. This hybridization of geometric and learning-driven planning strategies bridges deterministic motion planning with adaptive decision intelligence, providing a scalable foundation for trustworthy UAV autonomy. The paper proceeds through theoretical formulation, experimental validation, and a comprehensive discussion on system performance and reliability across diverse flight conditions.},
        keywords = {UAV Path Planning, Guided RRT*, Enhanced RRT*, Machine Learning, Reliability Estimation, Dynamic Obstacles, Autonomous Navigation, Behavioural Stability},
        month = {},
        }