Online Motion Planning for Quadrotor Multi-Point Navigation Using Efficient Imitation Learning-Based Strategy

Abstract

Over the past decade, there has been a remarkable surge in utilizing quadrotors for various purposes due to their simple structure and aggressive maneuverability. One of the key challenges is online time-optimal trajectory generation and control technique. This paper proposes an imitation learning-based online solution to efficiently navigate the quadrotor through multiple waypoints with near-time-optimal performance. The neural networks (WN&CNets) are trained to learn the control law from the dataset generated by the time-consuming CPC algorithm and then deployed to generate the optimal control commands online to guide the quadrotors. To address the challenge of limited training data and the hover maneuver at the final waypoint, we propose a transition phase strategy that utilizes MINCO trajectories to help the quadrotor ‘jump over’ the stop-and-go maneuver when switching waypoints. Our method is demonstrated in both simulation and real-world experiments, achieving a maximum speed of 5.6m/s while navigating through 7 waypoints in a confined space of 5.5m × 5.5m × 2.0m [video<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup>]. The results show that with a slight loss in optimality, the WN&CNets significantly reduce the processing time and enable online control for multi-point flight tasks.