Image-Based Visual Servoing Controller for Multirotor Aerial Robots Using Deep Reinforcement Learning

Carlos Sampedro; Alejandro Rodriguez-Ramos; Ignacio Gil; Luis Mejias; Pascual Campoy

IROS 2018poster74 citations

Image-Based Visual Servoing Controller for Multirotor Aerial Robots Using Deep Reinforcement Learning

Carlos Sampedro, Alejandro Rodriguez-Ramos, Ignacio Gil, Luis Mejias, Pascual Campoy

Abstract

In this paper, we propose a novel Image-Based Visual Servoing (IBVS) controller for multirotor aerial robots based on a recent deep reinforcement learning algorithm named Deep Deterministic Policy Gradients (DDPG). The proposed RL-IBVS controller is successfully trained in a Gazebo-based simulation scenario in order to learn the appropriate IBVS policy for directly mapping a state, based on errors in the image, to the linear velocity commands of the aerial robot. A thorough validation of the proposed controller has been conducted in simulated and real flight scenarios, demonstrating outstanding capabilities in object following applications. Moreover, we conduct a detailed comparison of the RL-IBVS controller with respect to classic and partitioned IBVS approaches.

BibTeX

@inproceedings{iros2018_imagebasedvisual,
  title = {Image-Based Visual Servoing Controller for Multirotor Aerial Robots Using Deep Reinforcement Learning},
  author = {Carlos Sampedro and Alejandro Rodriguez-Ramos and Ignacio Gil and Luis Mejias and Pascual Campoy},
  booktitle = {IROS 2018},
  year = {2018}
}