ForschungPublikationen
Dueling Double Deep Q-Network for indoor exploration in factory environments with an unmanned aircraft system

Dueling Double Deep Q-Network for indoor exploration in factory environments with an unmanned aircraft system

Kategorien Konferenz (reviewed)
Jahr 2023
Autoren A. Seel, F. Kreutzjans, B. Küster, M. Stonis and L. Overmeyer
Veröffentlicht in 22nd International Symposium INFOTEH-JAHORINA (INFOTEH), East Sarajevo, Bosnia and Herzegovina, pp. 1-6
Beschreibung

Although factory planning is widely recognized as a way to significantly enhance manufacturing productivity, the associated costs in terms of time and money can be prohibitive. In this paper, we present a solution to this challenge through the development of a Software-in-the-loop (SITL) framework that leverages an Unmanned Aircraft System (UAS) in an autonomous capacity. The framework incorporates simulated sensors, a UAS, and a virtual factory environment. Moreover, we propose a Deep Reinforcement Learning (DRL) agent that is capable of collision avoidance and exploration using the Dueling Double Deep Q-Network (3DQN) with prioritized experience replay.

DOI 10.1109/INFOTEH57020.2023.10094171