TY - JOUR
T1 - Efficient and Robust Time-Optimal Trajectory Planning and Control for Agile Quadrotor Flight
AU - Zhou, Ziyu
AU - Wang, Gang
AU - Sun, Jian
AU - Wang, Jikai
AU - Chen, Jie
N1 - Publisher Copyright:
© 2016 IEEE.
PY - 2023/12/1
Y1 - 2023/12/1
N2 - Agile quadrotor flight relies on rapidly planning and accurately tracking time-optimal trajectories, a technology critical to their application in the wild. However, the computational burden of computing time-optimal trajectories based on the full quadrotor dynamics (typically on the order of minutes or even hours) can hinder its ability to respond quickly to changing scenarios. Additionally, modeling errors and external disturbances can lead to deviations from the desired trajectory during tracking in real time. This letter proposes a novel approach to computing time-optimal trajectories, by fixing the nodes with waypoint constraints and adopting separate sampling intervals for trajectories between waypoints, which significantly accelerates trajectory planning. Furthermore, the planned paths are tracked via a time-adaptive model predictive control scheme whose allocated tracking time can be adaptively adjusted on-the-fly, therefore enhancing the tracking accuracy and robustness. We evaluate our approach through simulations and experimentally validate its performance in dynamic waypoint scenarios for time-optimal trajectory replanning and trajectory tracking.
AB - Agile quadrotor flight relies on rapidly planning and accurately tracking time-optimal trajectories, a technology critical to their application in the wild. However, the computational burden of computing time-optimal trajectories based on the full quadrotor dynamics (typically on the order of minutes or even hours) can hinder its ability to respond quickly to changing scenarios. Additionally, modeling errors and external disturbances can lead to deviations from the desired trajectory during tracking in real time. This letter proposes a novel approach to computing time-optimal trajectories, by fixing the nodes with waypoint constraints and adopting separate sampling intervals for trajectories between waypoints, which significantly accelerates trajectory planning. Furthermore, the planned paths are tracked via a time-adaptive model predictive control scheme whose allocated tracking time can be adaptively adjusted on-the-fly, therefore enhancing the tracking accuracy and robustness. We evaluate our approach through simulations and experimentally validate its performance in dynamic waypoint scenarios for time-optimal trajectory replanning and trajectory tracking.
KW - Control architectures and programming
KW - integrated planning and control
KW - optimization and optimal control
UR - http://www.scopus.com/inward/record.url?scp=85174825092&partnerID=8YFLogxK
U2 - 10.1109/LRA.2023.3322075
DO - 10.1109/LRA.2023.3322075
M3 - Article
AN - SCOPUS:85174825092
SN - 2377-3766
VL - 8
SP - 7913
EP - 7920
JO - IEEE Robotics and Automation Letters
JF - IEEE Robotics and Automation Letters
IS - 12
ER -