Research status and prospects of perception technology for unmanned mining vehicle driving environment
| Autor: | HU Qingsong, MENG Chunlei, LI Shiyin, SUN Yanjing |
|---|---|
| Jazyk: | čínština |
| Rok vydání: | 2023 |
| Předmět: | |
| Zdroj: | Gong-kuang zidonghua, Vol 49, Iss 6, Pp 128-140 (2023) |
| Druh dokumentu: | article |
| ISSN: | 1671-251X 1671-251x |
| DOI: | 10.13272/j.issn.1671-251x.18115 |
| Popis: | The auxiliary transportation system for coal mine is an essential system for transporting personnel, important materials, and equipment in coal mine enterprises. Realizing unmanned driving in coal mine is an inevitable requirement for improving transportation efficiency and ensuring transportation safety, and is also the only way to implement the national coal mine intelligent construction deployment. The mine unmanned driving relies on accurate and real-time environmental perception. By using onboard perception devices such as LiDAR and millimeter wave radar, as well as collaborative perception supported by the Internet of vehicles, the precise and detailed perception of local vehicles and even the entire mine is achieved. A systematic review is conducted on the research status of unmanned driving environment perception technology in mines. It is pointed out that the special environment of coal mine will lead to varying degrees of degradation in the performance of mine onboard perception devices. The advantages and disadvantages of various onboard perception devices are summarized. The key technologies of mine unmanned driving environment perception are elaborated in detail. The technologies include single-sensor obstacle recognition methods based on visible light images or laser point clouds, the classification of multi-sensor fusion perception, and multi-sensor fusion methods such as visible light images+laser point clouds, visible light images+millimeter wave point clouds, visible light images+laser point clouds+millimeter wave point clouds, 4D millimeter wave radar+other perception devices. The technologies include the implementation, data processing methods of intelligent networked collaborative perception, and their promoting effects on unmanned driving. The technologies also include methods for detecting and recognizing traffic signs in underground roadways, and methods for segmenting the driving area of underground trackless rubber wheeled vehicles and tracked locomotives in roadways. The development direction of unmanned driving environment perception technology in mines is pointed out. It is recommended to improve the fusion performance of multiple sensors in mines, study adaptive perception algorithms in mines, and break through the intelligent networked collaborative perception technology in mines. |
| Databáze: | Directory of Open Access Journals |
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