论文标题
使用深光流动动力学和PixelMPC的积极感知感知导航
Aggressive Perception-Aware Navigation using Deep Optical Flow Dynamics and PixelMPC
论文作者
论文摘要
最近，通过利用机器学习的力量，基于视觉的控制已获得了吸引力。在这项工作中，我们将模型预测控制（MPC）框架与视觉管道相结合。我们引入了深度流动（DOF）动力学，这是光流和机器人动力学的组合。使用DOF动力学，MPC明确地将相关像素的预测运动纳入了机器人的计划轨迹中。我们对DOF的实现是记忆效率，数据效率和计算便宜的，因此可以实时计算以在MPC框架中使用。建议的像素模型预测控制（PixelMPC）算法控制机器人以完成高速赛车任务，同时保持重要特征（门）的可见性。这提高了基于视觉的估计器的本地化可靠性，并最终可能导致安全的自动飞行。所提出的算法在具有高速无人机赛车任务的影像现实主义模拟中测试。
Recently, vision-based control has gained traction by leveraging the power of machine learning. In this work, we couple a model predictive control (MPC) framework to a visual pipeline. We introduce deep optical flow (DOF) dynamics, which is a combination of optical flow and robot dynamics. Using the DOF dynamics, MPC explicitly incorporates the predicted movement of relevant pixels into the planned trajectory of a robot. Our implementation of DOF is memory-efficient, data-efficient, and computationally cheap so that it can be computed in real-time for use in an MPC framework. The suggested Pixel Model Predictive Control (PixelMPC) algorithm controls the robot to accomplish a high-speed racing task while maintaining visibility of the important features (gates). This improves the reliability of vision-based estimators for localization and can eventually lead to safe autonomous flight. The proposed algorithm is tested in a photorealistic simulation with a high-speed drone racing task.
