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

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.