In this work, a novel, end-to-end motion planning method is proposed for quadrotor navigation in cluttered environments. The proposed method circumvents the explicit sensing-reconstructing-planning in contrast to conventional navigation algorithms. It uses raw depth images obtained from a front-facing camera and directly generates local motion plans in the form of smooth motion primitives that move a quadrotor to a goal by avoiding obstacles. Promising training and testing results are presented in both AirSim simulations and real flights with DJI F330 Quadrotor equipped with Intel RealSense D435. The proposed system in action can be found in https://youtu.be/pYvKhc8wrTM.
@article{arxiv.1909.13599,
title = {End-to-End Motion Planning of Quadrotors Using Deep Reinforcement Learning},
author = {Efe Camci and Erdal Kayacan},
journal= {arXiv preprint arXiv:1909.13599},
year = {2019}
}
Comments
IROS 2019 Workshop, Learning Representations for Planning and Control