Version 1
: Received: 22 April 2024 / Approved: 23 April 2024 / Online: 23 April 2024 (14:01:07 CEST)
How to cite:
Lin, H.; Chang, K.; Huang, H. Development of Unmanned Aerial Vehicle Navigation and Warehouse Inventory System Based on Reinforcement Learning. Preprints2024, 2024041526. https://doi.org/10.20944/preprints202404.1526.v1
Lin, H.; Chang, K.; Huang, H. Development of Unmanned Aerial Vehicle Navigation and Warehouse Inventory System Based on Reinforcement Learning. Preprints 2024, 2024041526. https://doi.org/10.20944/preprints202404.1526.v1
Lin, H.; Chang, K.; Huang, H. Development of Unmanned Aerial Vehicle Navigation and Warehouse Inventory System Based on Reinforcement Learning. Preprints2024, 2024041526. https://doi.org/10.20944/preprints202404.1526.v1
APA Style
Lin, H., Chang, K., & Huang, H. (2024). Development of Unmanned Aerial Vehicle Navigation and Warehouse Inventory System Based on Reinforcement Learning. Preprints. https://doi.org/10.20944/preprints202404.1526.v1
Chicago/Turabian Style
Lin, H., Kai-Lun Chang and Hsin-Ying Huang. 2024 "Development of Unmanned Aerial Vehicle Navigation and Warehouse Inventory System Based on Reinforcement Learning" Preprints. https://doi.org/10.20944/preprints202404.1526.v1
Abstract
In this paper we present the exploration of indoor positioning technologies for UAVs, and navigation techniques for path planning and obstacle avoidance. The objective is to perform warehouse inventory tasks using a drone to search for barcodes or markers to identify the objects. For the indoor positioning techniques, we employ Visual-Inertial Odometry (VIO), Ultra-Wideband (UWB), AprilTag fiducial markers, and Simultaneous Localization and Mapping (SLAM). These algorithms encompass global positioning, local positioning, and pre-mapping positioning, comparing the merits and drawbacks of various techniques and trajectories. In UAV navigation, we combine SLAM-based RTAB-Map indoor mapping and navigation path planning of the ROS for indoor environments. This system enables precise drone positioning indoors and utilizes global and local path planners to generate flight paths that avoid dynamic, static, unknown, and known obstacles, demonstrating high practicality and feasibility. To achieve the warehouse inventory inspection, a reinforcement learning approach is proposed to recognize the markers by adjusting the UAV's viewpoint. We address several main problems in inventory management, including efficiently planning paths while ensuring a certain level of detection rate. Two reinforcement learning techniques, AC (Actor-Critic) and PPO (Proximal Policy Optimization), are implemented based on AprilTag identification. Testing is performed in both simulated and real-world environments, and the effectiveness of the proposed method is validated.Code is available at https://github.com/kellen080/Navigation and https://github.com/kellen080/Indoor\_Positioning.
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
