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

Type of publication:  Artikel
Publikationsstatus: Veröffentlicht
Zeitschrift: Applied Sciences special issue "Intelligent Robotics"
Band: 12
Nummer: 6
Jahr: 2022
Monat: März
Seiten: 3153
DOI: https://doi.org/10.3390/app12063153
Abriss: 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%.
Schlagworte: automatic curriculum learning, autonomous navigation, deep reinforcement learning, multi-modal sensor perception
Autoren: Xue, Honghu
Hein, Benedikt
Mohamed, Bakr
Schildbach, Georg
Abel, Bengt
Rueckert, Elmar
Herausgeber: Bonciu, Yasmine
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