Imitation Learning-Based Online Time-Optimal Control with Multiple-Waypoint Constraints for Quadrotors
CoRR(2024)
摘要
Over the past decade, there has been a remarkable surge in utilizing
quadrotors for various purposes due to their simple structure and aggressive
maneuverability, such as search and rescue, delivery and autonomous drone
racing, etc. One of the key challenges preventing quadrotors from being widely
used in these scenarios is online waypoint-constrained time-optimal trajectory
generation and control technique. This letter proposes an imitation
learning-based online solution to efficiently navigate the quadrotor through
multiple waypoints with 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 polynomials 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 7 m/s while navigating through 7 waypoints in a confined space of 6.0
m * 4.0 m * 2.0 m. The results show that with a slight loss in optimality, the
WN CNets significantly reduce the processing time and enable online optimal
control for multiple-waypoint-constrained flight tasks.
更多查看译文
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要