We tackle the problem of planning a minimum-time trajectory for a quadrotor over a sequence of specified waypoints in the presence of obstacles while exploiting the full quadrotor dynamics. This problem is crucial for autonomous search-and-rescue and drone-racing scenarios but was, so far, unaddressed by the robotics community in its entirety due to the challenges of minimizing time in the presence of the non-convex constraints posed by collision avoidance. Early works relied on simplified dynamics or polynomial trajectory representations that did not exploit the full actuator potential of a quadrotor and, thus, did not aim at minimizing time. We address this challenging
problem by using a hierarchical, sampling-based method with an incrementally more complex quadrotor model. Our method first finds paths in different topologies to guide subsequent trajectory search for a kinodynamic point-mass model. Then, it uses an asymptotically-optimal, kinodynamic sampling-based method based on a full quadrotor model on top of the point-mass solution to find a feasible trajectory with a time-optimal objective. The proposed method is shown to outperform all related baselines in cluttered environments and is further validated in real-world flights at over 60km/h in one of the world’s largest motion capture systems. We release the code open source.

Reference
R. Penicka, D.Scaramuzza
"Minimum-Time Quadrotor Waypoint Flight in Cluttered Environments"
IEEE Robotics and Automation Letters, 2022.
PDF: [ Ссылка ]
Code: [ Ссылка ]

More on our research in Agile Drone Flight: [ Ссылка ]
More on our research in Autonomous Drone Racing: [ Ссылка ]

Affiliations:
R. Penicka, D.Scaramuzza are with the Robotics and Perception Group, Dep. of Informatics, University of Zurich, and Dep. of Neuroinformatics, University of Zurich and ETH Zurich, Switzerland
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