A Physics-Informed Neural Network Approach for UAV Path Planning in Dynamic Environments

Abstract:Unmanned aerial vehicles (UAVs) operating in dynamic wind fields must generate safe and energy-efficient trajectories under physical and environmental constraints. Traditional planners, such as A* and kinodynamic RRT*, often yield suboptimal or non-smooth paths due to discretization and sampling limitations. This paper presents a physics-informed neural network (PINN) framework that embeds UAV dynamics, wind disturbances, and obstacle avoidance directly into the learning process. Without requiring supervised data, the PINN learns dynamically feasible and collision-free trajectories by minimizing physical residuals and risk-aware objectives. Comparative simulations show that the proposed method outperforms A* and Kino-RRT* in control energy, smoothness, and safety margin, while maintaining similar flight efficiency. The results highlight the potential of physics-informed learning to unify model-based and data-driven planning, providing a scalable and physically consistent framework for UAV trajectory optimization.

Robotics (cs.RO); Artificial Intelligence (cs.AI)

arXiv:2510.21874 [cs.RO]

(or arXiv:2510.21874v2 [cs.RO] for this version)

https://doi.org/10.48550/arXiv.2510.21874 Focus to learn more arXiv-issued DOI via DataCite

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