Aggressive Perception-Aware Navigation Using Deep Optical Flow Dynamics and PixelMPC
Keuntaek Lee, Jason Gibson, Evangelos A. Theodorou
Abstract
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.
BibTeX
@inproceedings{ral2020_aggressivepercep,
title = {Aggressive Perception-Aware Navigation Using Deep Optical Flow Dynamics and PixelMPC},
author = {Keuntaek Lee and Jason Gibson and Evangelos A. Theodorou},
booktitle = {RA-L 2020},
year = {2020}
}