期刊
ELECTRONICS
卷 11, 期 8, 页码 -出版社
MDPI
DOI: 10.3390/electronics11081228
关键词
Internet of Things; UAV/UGV mobile collaboration; object detection; image recognition; drone-aided path planning
资金
- Ministry of Science and Technology, Taiwan
This study proposes a fast drone-aided path planning approach to help unmanned ground vehicles (UGVs) traverse unfamiliar areas with obstacles. The approach uses a one-stage object detection and image recognition algorithm to identify obstacles and plan an optimal route. Experiments show that the method has a short execution time and does not affect the image quality of the path map, allowing users to drive UGVs through unfamiliar areas with obstacles effectively.
Even with visual contact equipment such as cameras, an unmanned ground vehicle (UGV) alone usually takes a lot of time to navigate an unfamiliar area with obstacles. Therefore, this study proposes a fast drone-aided path planning approach to help UGVs traverse an unfamiliar area with obstacles. In this scenario called UAV/UGV mobile collaboration (abbreviated UAGVMC), a UGV initially invokes an unmanned aerial vehicle (UAV) at the scene to take a ground image and send it back to the cloud to proceed with object detection, image recognition, and path planning (abbreviated odirpp). The cloud then sends the UGV a well-planned path map to help traverse an unfamiliar area. This approach uses the one-stage object detection and image recognition algorithm YOLOv4-CSP to quickly and accurately identify obstacles and the New Bidirectional A* (NBA*) algorithm to plan an optimal route avoiding ground objects. Experiments show that the execution time of path planning for each scene is less than 10 s on average. It does not affect the image quality of the path map. It ensures that the user can correctly interpret the path map and remotely drive the UGV rapidly, passing through that unfamiliar area with obstacles. As a result, the selected model can outperform the other alternatives significantly by average performance ratio up to 3.87 times on average.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据