Technology Category
Planning, Routing, and Scheduling: Replanning and Plan RepairSearch and Optimization: Sampling/Simulation-based SearchIntelligent Robots: Motion and Path Planning
A Physics-Informed Neural Network Approach for UAV Path Planning in Dynamic Environments
🤖 AI Summary
To address the challenge of simultaneously ensuring safety, energy efficiency, and trajectory smoothness for UAVs operating in dynamic wind fields, this paper proposes a physics-informed neural network (PINN) framework. The method explicitly encodes the UAV’s six-degree-of-freedom dynamics, wind disturbance models, and obstacle constraints into the neural architecture, enabling unsupervised end-to-end optimization via minimization of physical residuals and a risk-aware objective—without requiring ground-truth trajectory labels. Its key innovation lies in the synergistic integration of PINN-based modeling with gradient-free sampling strategies, unifying model-driven and data-driven paradigms. Experimental results demonstrate that the proposed approach reduces energy consumption by 12.7%, decreases trajectory jitter by 38.5%, and improves minimum safety distance by a factor of 2.1, while maintaining flight efficiency comparable to Kino-RRT*. It significantly outperforms A* and conventional sampling-based planners.
Technology Category
Application Category
📝 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.
Problem
Research questions and friction points this paper is trying to address.
Planning UAV trajectories in dynamic wind environments
Overcoming suboptimal paths from traditional discretization methods
Integrating physics constraints into neural network learning process
Innovation
Methods, ideas, or system contributions that make the work stand out.
Physics-informed neural network embeds UAV dynamics and constraints
Learns collision-free trajectories without supervised data
Minimizes physical residuals and risk-aware objectives
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