Three-dimensional Trajectory Optimization for Quadrotor Tail-sitter UAVs: Traversing through Given Waypoints

TL;DR

This paper presents a novel 3D trajectory optimization method for quadrotor tail-sitter UAVs, enabling smooth and agile transitions through waypoints by integrating differential flatness, MINCO, and MPC techniques.

Contribution

It introduces a unified 3D trajectory optimization framework that eliminates differential constraints and enhances feasibility and performance over existing methods.

Findings

Outperforms L1 Guidance Law and Dubins path in simulations.

Ensures trajectory feasibility with soft speed constraints.

Enables smooth 3D transitions for tail-sitter UAVs.

Abstract

Given the evolving application scenarios of current fixed-wing unmanned aerial vehicles (UAVs), it is necessary for UAVs to possess agile and rapid 3-dimensional flight capabilities. Typically, the trajectory of a tail-sitter is generated separately for vertical and level flights. This limits the tail-sitter's ability to move in a 3-dimensional airspace and makes it difficult to establish a smooth transition between vertical and level flights. In the present work, a 3-dimensional trajectory optimization method is proposed for quadrotor tail-sitters. Especially, the differential dynamics constraints are eliminated when generating the trajectory of the tail-sitter by utilizing differential flatness method. Additionally, the temporal parameters of the trajectory are generated using the state-of-the-art trajectory generation method called MINCO (minimum control). Subsequently, we convert the speed constraint on the vehicle into a soft constraint by discretizing the trajectory in time. This increases the likelihood that the control input limits are satisfied and the trajectory is feasible. Then, we utilize a kind of model predictive control (MPC) method to track trajectories. Even if restricting the tail-sitter's motion to a 2-dimensional horizontal plane, the solutions still outperform those of the L1 Guidance Law and Dubins path.