This paper proposes an algorithm for motion planning among dynamic agents using adaptive conformal prediction. We consider a deterministic control system and use trajectory predictors to predict the dynamic agents' future motion, which is assumed to follow an unknown distribution. We then leverage ideas from adaptive conformal prediction to dynamically quantify prediction uncertainty from an online data stream. Particularly, we provide an online algorithm uses delayed agent observations to obtain uncertainty sets for multistep-ahead predictions with probabilistic coverage. These uncertainty sets are used within a model predictive controller to safely navigate among dynamic agents. While most existing data-driven prediction approached quantify prediction uncertainty heuristically, we quantify the true prediction uncertainty in a distribution-free, adaptive manner that even allows to capture changes in prediction quality and the agents' motion. We empirically evaluate of our algorithm on a simulation case studies where a drone avoids a flying frisbee.
翻译:本文提出动态物剂使用适应性一致预测进行运动规划的算法。 我们考虑确定性控制系统, 并使用轨迹预测器来预测动态物剂的未来运动, 假设这种运动会随未知的分布而变化。 然后我们利用从适应性一致预测到动态数据流预测不确定的动态物剂动态量化的构想。 特别是, 我们提供在线算法, 利用延迟性物剂观测来获得具有概率覆盖的多步头预测的不确定性数据集。 这些不确定性数据集用于模型预测控制器, 以便在动态物剂之间安全航行。 虽然大多数现有的数据驱动预测器都以超常方式量化预测的不确定性, 我们用无分配性、 适应性的方式量化真实的预测不确定性, 甚至能够捕捉预测质量的变化和代理人运动。 我们通过实验性评估模拟案例研究的算法, 无人机避免飞行飞盘。