A Deep Reinforcement Learning Visual Servoing Control Strategy for Target Tracking Using a Multirotor UAV

Andreas Mitakidis, Sotirios N. Aspragkathos, Fotis Panetsos, George C. Karras, Kostas J. Kyriakopoulos

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In this work, a deep Reinforcement Learning control scheme is developed in order to execute autonomous tracking of an unmanned ground vehicle (UGV) with a multirotor unmanned aerial vehicle (UAV). The UAV is equipped with a downward looking camera and the detection of the target UGV is achieved through a convolutional neural network (CNN). The deep RL control policy is deployed as a cascade position controller, which commands the inner attitude controller of the autopilot, and achieves the following of the UGV despite the aggressive maneuvers of the target. The efficacy of the proposed framework is demonstrated via a set of outdoor experiments using an octocopter flying above the ground vehicle.

Original languageEnglish (US)
Title of host publication2023 9th International Conference on Automation, Robotics and Applications, ICARA 2023
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages219-224
Number of pages6
ISBN (Electronic)9781665489218
DOIs
StatePublished - 2023
Event9th International Conference on Automation, Robotics and Applications, ICARA 2023 - Abu Dhabi, United Arab Emirates
Duration: Feb 10 2023Feb 12 2023

Publication series

Name2023 9th International Conference on Automation, Robotics and Applications, ICARA 2023

Conference

Conference9th International Conference on Automation, Robotics and Applications, ICARA 2023
Country/TerritoryUnited Arab Emirates
CityAbu Dhabi
Period2/10/232/12/23

Keywords

  • reinforcement learning
  • target tracking
  • visual servoing

ASJC Scopus subject areas

  • Computer Science Applications
  • Mechanical Engineering
  • Control and Optimization
  • Artificial Intelligence

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