🤖 AI Summary
Tiltrotor quadcopters suffer from unsmooth transitions between vertical takeoff/landing and horizontal cruise during 3D waypoint navigation, resulting in low trajectory feasibility and poor satisfaction of dynamical and control constraints.
Method: This paper proposes a differential-flatness-based MINCO (Minimum Control) 3D trajectory optimization framework. It uniquely integrates differential flatness with the MINCO principle and introduces a time-discretization strategy with softened velocity constraints to generate globally smooth, singularity-free, and dynamics-consistent trajectories across the entire state space. Real-time high-precision tracking is achieved via model predictive control (MPC).
Results: Experiments demonstrate superior performance over L1 guidance and Dubins paths under 2D constraints. The method strictly satisfies full-state dynamics and actuator limits throughout flight, significantly improving trajectory feasibility and control-input constraint satisfaction. It enables agile, robust, and high-precision autonomous flight.
📝 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.