Work

Multi-stage robust optimization for a class of UAV trajectory planning problems with uncertain nonlinear dynamics

Year: 2025

Type: article

Abstract: Trajectory planning under uncertain dynamics is critical for safety-critical systems like Unmanned Aerial Vehicles (UAVs), where uncertainties in aerodynamic force and control surface failure can lead to mission failure. This paper proposes a Multi-stage Robust Optimization (MRO) framework to address nonlinear trajectory planning with bounded but unknown parameters. By integrating first-order sensitivity analysis and sequential optimization, the proposed method ensures robustness against worst-case parameter deviations while maintaining high terminal accuracy. Unlike existing approaches, this paper explicitly quantifies uncertainty propagation through sensitivity bounds and divides long-term planning into sub-stages to reduce cumulative errors. Simulations on a UAV model with uncertainties in aerodynamic coefficients, wind fields and coefficients of control inputs demonstrate that MRO achieves high terminal state accuracy and strong robustness.

Source: Chinese Journal of Aeronautics

DOI: 10.1016/j.cja.2025.103771

Authors: Zixin FENG, Wenchao XUE, Ran ZHANG, Huifeng LI

Institutions: Chinese Academy of Sciences, Academy of Mathematics and Systems Science, University of Chinese Academy of Sciences, Beihang University

Language: English

Topic: Robotic Path Planning Algorithms

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

Estimated APC paid: $2,000

Funders: National Natural Science Foundation of China, Chinese Academy of Sciences, Youth Innovation Promotion Association of the Chinese Academy of Sciences, Youth Innovation Promotion Association