Using Deep Reinforcement Learning with Automatic Curriculum Learning for Mapless Navigation in Intralogistics

Publikationen: Beitrag in FachzeitschriftArtikelForschung(peer-reviewed)

Autoren

  • Honghu Xue
  • Benedikt Hein
  • Mohamed Bakr
  • Georg Schildbach
  • Bengt Abel

Externe Organisationseinheiten

  • Universität Lübeck
  • KION Group AG

Abstract

We propose a deep reinforcement learning approach for solving a mapless navigation problem in warehouse scenarios. In our approach, an automatic guided vehicle is equipped with two LiDAR sensors and one frontal RGB camera and learns to perform a targeted navigation task. The challenges reside in the sparseness of positive samples for learning, multi-modal sensor perception with partial observability, the demand for accurate steering maneuvers together with long training cycles. To address these points, we propose NavACL-Q as an automatic curriculum learning method in combination with a distributed version of the soft actor-critic algorithm. The performance of the learning algorithm is evaluated exhaustively in a different warehouse environment to validate both robustness and generalizability of the learned policy. Results in NVIDIA Isaac Sim demonstrates that our trained agent significantly outperforms the map-based navigation pipeline provided by NVIDIA Isaac Sim with an increased agent-goal distance of 3 m and a wider initial relative agent-goal rotation of approximately 45∘. The ablation studies also suggest that NavACL-Q greatly facilitates the whole learning process with a performance gain of roughly 40% compared to training with random starts and a pre-trained feature extractor manifestly boosts the performance by approximately 60%.

Details

OriginalspracheEnglisch
Aufsatznummer3153
Seitenumfang30
FachzeitschriftApplied Sciences (Switzerland)
Jahrgang12.2022
Ausgabenummer6
DOIs
StatusVeröffentlicht - 19 Mär 2022