A numerically-stable trajectory generation and optimization algorithm for autonomous quadrotor UAVs - Alqudsi, Yunes; Makaraci, Murat; Kassem, Ayman; El-Bayoumi, Gamal (Robotics and autonomous systems, 2023)
This paper introduces a novel trajectory generation and optimization algorithm (TGO) that enables agile and aggressive flight of quadrotor UAVs while considering various constraints associated with robot dynamics, actuator inputs, and flight environment. The TGO algorithm employs time-parametrized polynomial trajectories based on a predetermined sequence of waypoints to produce dynamically feasible and collision-free trajectories. This approach extends previous work that utilized differential flatness property and polynomial based trajectories by eliminating the need for iterative searching and computationally intensive sampling. One of the significant advantages of the TGO algorithm is its numerical stability for large number of waypoints and high-order polynomials. To address the ill-conditioned problem of Quadratic Programming (QP) based methods, the TGO algorithm reformulates the trajectory generation and optimization problem into an unconstrained quadratic programming (UCP) using the numerically stable null-space factorization method. The TGO algorithm produces minimum derivative trajectories and minimum waypoints arrival times, generating a wide range of aggressive trajectories that can leverage the full maneuvering capabilities of quadrotor robots. The proposed algorithm’s numerical stability and computational advantages are demonstrated through various scenarios and comparisons. An animated simulation of the TGO algorithm is available at: https://youtu.be/MvvhBG14iIg. •The TGO algorithm enables quadrotor UAVs to fly with aggressive and agile movements.•The TGO algorithm takes into account the dynamics of the quadrotor, the flying environment, and actuator limitations.•The TGO algorithm eliminates the need for intensive computation and searching.•The TGO algorithm can also be used to effectively solve the Unconstrained Quadratic Programming problems.•The algorithm enhances numerical stability for trajectories with a large number of waypoints and high-order polynomials.