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

arXiv: https://arxiv.org/abs/2510.21874 · HTML: https://mlsanity.com/paper/2510.21874/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 32 references that are in the map.
- First posted on arXiv 23 Oct 2025.

## Builds on (most cited first, 4 of 4)

- [DL-DRL: A double-level deep reinforcement learning approach for large-scale task scheduling of multi-UAV](https://mlsanity.com/paper/2208.02447/) (2022)
- [Vision-Based Environmental Perception for Autonomous Driving](https://mlsanity.com/paper/2212.11453/) (2022)
- [Advances in UAV Avionics Systems Architecture, Classification and Integration: A Comprehensive Review and Future Perspectives](https://mlsanity.com/paper/2501.00856/) (2025)
- [Automated Lane Change Behavior Prediction and Environmental Perception Based on SLAM Technology](https://mlsanity.com/paper/2404.04492/) (2024)

## Abstract (arXiv)

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.