Safe flight corridor constrained sequential convex programming for efficient trajectory generation of fixed-wing UAVs

• Published in: Chinese journal of aeronautics Vol. 38; no. 1; pp. 103174 - 550
• Main Authors: SUN, Jing, XU, Guangtong, WANG, Zhu, LONG, Teng, SUN, Jingliang
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.01.2025; School of Aerospace Engineering,Beijing Institute of Technology,Beijing 100081,China%Huzhou Institute,Zhejiang University,Huzhou 313000,China%Department of Automation,North China Electric Power University(Baoding),Baoding 071003,China
• Subjects: Customized convex optimizer; Efficient trajectory planning; Fixed-wing unmanned aerial vehicle; Safe flight corridor; Sequential convex programming; Efficient trajectory planning; Sequential convex programming; Fixed-wing unmanned aerial vehicle; Customized convex optimizer; Safe flight corridor; Sequential convex program-ming
• ISSN: 1000-9361; 2588-9230
• DOI: 10.1016/j.cja.2024.08.005

Generating dynamically feasible trajectory for fixed-wing Unmanned Aerial Vehicles (UAVs) in dense obstacle environments remains computationally intractable. This paper proposes a Safe Flight Corridor constrained Sequential Convex Programming (SFC-SCP) to improve the computation efficiency and reliability of trajectory generation. SFC-SCP combines the front-end convex polyhedron SFC construction and back-end SCP-based trajectory optimization. A Sparse A* Search (SAS) driven SFC construction method is designed to efficiently generate polyhedron SFC according to the geometric relation among obstacles and collision-free waypoints. Via transforming the nonconvex obstacle-avoidance constraints to linear inequality constraints, SFC can mitigate infeasibility of trajectory planning and reduce computation complexity. Then, SCP casts the nonlinear trajectory optimization subject to SFC into convex programming subproblems to decrease the problem complexity. In addition, a convex optimizer based on interior point method is customized, where the search direction is calculated via successive elimination to further improve efficiency. Simulation experiments on dense obstacle scenarios show that SFC-SCP can generate dynamically feasible safe trajectory rapidly. Comparative studies with state-of-the-art SCP-based methods demonstrate the efficiency and reliability merits of SFC-SCP. Besides, the customized convex optimizer outperforms off-the-shelf optimizers in terms of computation time.