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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: | |
| Herausgeber: | |
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