In this work, we explore a data-driven learning-based approach to learning
the kinodynamic model of a small autonomous vehicle, and observe the effect it
has on motion planning, specifically autonomous drifting. When executing a
motion plan in the real world, there are numerous causes for error, and what is
planned is often not what is executed on the actual car. Learning a kinodynamic
planner based off of inertial measurements and executed commands can help us
learn the world state. In our case, we look towards the realm of drifting; it
is a complex maneuver that requires a smooth enough surface, high enough speed,
and a drastic change in velocity. We attempt to learn the kinodynamic model for
these drifting maneuvers, and attempt to tighten the slip of the car. Our
approach is able to learn a kinodynamic model for high-speed circular
navigation, and is able to avoid obstacles on an autonomous drift at high speed
by correcting an executed curvature for loose drifts. We seek to adjust our
kinodynamic model for success in tighter drifts in future work.

通过数据驱动的学习方法，我们研究自主小型车辆的运动学模型，并观察其对运动规划（特别是自主漂移）的影响。我们基于惯性测量和执行命令来学习运动学规划器，以帮助我们了解世界状态。我们的研究主要关注漂移，并试图学习这些漂移动作的运动学模型，以及尝试减小车辆空转的滑动。我们的方法能够学习到高速圆形导航的运动学模型，并能够通过纠正松散漂移的曲率对高速漂移中的障碍物进行避让。我们将在未来的工作中调整我们的运动学模型以获得更好的紧急漂移效果。

学习自主车辆漂移的逆运动学动力学

Learning Inverse Kinodynamics for Autonomous Vehicle Drifting

In this paper, we study the one-shot and lifelong versions of the Target
Assignment and Path Finding problem in automated sortation centers, where each
agent needs to constantly assign itself a sorting station, move to its assigned
station without colliding with obstacles or other agents, wait in the queue of
that station to obtain a parcel for delivery, and then deliver the parcel to a
sorting bin. The throughput of such centers is largely determined by the total
idle time of all stations since their queues can frequently become empty. To
address this problem, we first formalize and study the one-shot version that
assigns stations to a set of agents and finds collision-free paths for the
agents to their assigned stations. We present efficient algorithms for this
task based on a novel min-cost max-flow formulation that minimizes the total
idle time of all stations in a fixed time window. We then demonstrate how our
algorithms for solving the one-shot problem can be applied to solving the
lifelong problem as well. Experimentally, we believe to be the first
researchers to consider real-world automated sortation centers using an
industrial simulator with realistic data and a kinodynamic model of real
robots. On this simulator, we showcase the benefits of our algorithms by
demonstrating their efficiency and effectiveness for up to 350 agents.

研究了自动分拣中心中一次分配分拣站点和路径规划问题，并提出了基于最小成本最大流形式化算法的有效解决方案。该方案不仅适用于一次性问题，还可用于长期问题。在真实数据和运动学模型的工业模拟器上进行的实验表明了该方案的高效性和有效性。