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 \emph{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.
翻译:我们处理的问题是,在障碍存在的情况下,如何规划一个针对一系列特定路径点的二次钻探的最小时间轨迹,同时利用完整的二次钻探动态。这个问题对于自主搜索和救援以及无人驾驶赛跑情景至关重要,但迄今为止,由于在避免碰撞造成的非闭塞制约下如何最大限度地减少时间的挑战,机器人社区(emph{sult 全文})尚未解决。早期工程依赖于简化的动态或多边轨道显示,没有利用二次钻探的完整启动器潜力,因此没有以最大限度地减少时间为目标。我们通过使用等级、基于抽样的方法来解决这一具有挑战性的问题,该方法具有渐进式更复杂的二次钻探模型模式。我们的方法首先在不同的地形中找到路径,用以指导随后在避免碰撞造成的非闭塞制约下点限制下的时间轨迹搜索。然后,它使用一种以全二次钻探模型为基础的、动态动态、亲近动力取样方法,该模型没有利用二次钻探器的潜力,因此没有以最大限度地减少时间为目的。我们用一个具有时间-最复杂的轨道来解决这个问题。我们的方法首先在60次的轨道上展示了世界最大的基线,然后展示了世界的基线。