Factory AGV detection and obstacle avoidance program design based on YOLOv8
This dissertation investigates and realizes a multi-sensor fusion-based path planning and obstacle avoidance system for automated guided vehicles (AGVs) in factory scenarios. The system combines LiDAR, IMU, and camera sensors for target detection via YOLOv8, and utilizes LIO-SAM and AMCL for high-pr...
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| Format: | Thesis-Master by Coursework |
| Language: | English |
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Nanyang Technological University
2024
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| Online Access: | https://hdl.handle.net/10356/179796 |