Trajectory Optimization with Collision Avoidance for Quadrotor Using Augmented Lagrangian iLQR

Avoidance of obstacles would be a significant problem in transportation systems using unmanned aerial vehicles. This paper describes the solution of the constrained nonlinear system of the quadrotor, which is represented by a particular state equation. The equation is that a quadrotor can simultaneously control rotational and translational dynamics. In previous studies, the quadrotor dynamics was controlled using an LQR controller. However, an LQR algorithm cannot take constraints, and control inputs saturate depending on the reference states of the terminal time. We mainly focus on the LQR controller and apply the Augmented Lagrangian iLQR algorithm to the state space equation. As a result, the quadrotor using an Augmented Lagrangian iLQR controller is capable of robust flight compared to an LQR controller. Furthermore, a quadrotor finds a collision-free path among many obstacles and avoids them. Numerical results show the potential of Augmented Lagrangian iLQR for real-time trajectory optimization.