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Design and Test of Obstacle Detection and Harvester Pre-Collision System Based on 2D Lidar

文献类型： 外文期刊

作者： Shang, Yehua 1 ; Wang, Hao 2 ; Qin, Wuchang 2 ; Wang, Qian 2 ; Liu, Huaiyu 4 ; Yin, Yanxin 2 ; Song, Zhenghe 1 ; Meng, Zhijun 2 ;

作者机构： 1.China Agr Univ, Coll Engn, Beijing 100083, Peoples R China

2.Beijing Acad Agr & Forestry Sci, Res Ctr Intelligent Equipment, Beijing 100097, Peoples R China

3.State Key Lab Intelligent Agr Power Equipment, Beijing 100097, Peoples R China

4.AgChip Sci & Technol Beijing Co Ltd, Beijing 100097, Peoples R China

关键词： lidar; obstacle detection; harvester; pre-collision system

期刊名称：AGRONOMY-BASEL （ 影响因子：3.7； 五年影响因子：4.0 ）

ISSN：

年卷期： 2023 年 13 卷 2 期

页码：

收录情况： SCI

摘要： Aiming at the need to prevent agricultural machinery from colliding with obstacles in the operation of unmanned agricultural machinery, an obstacle detection algorithm using 2D lidar was proposed, and a pre-collision system was designed using this algorithm, which was tested on a harvester. The method uses the differences between lidar data frames to calculate the collision times between the farm machinery and the obstacles. The algorithm consists of the following steps: pre-processing to determine the region of interest, median filtering, and DBSCAN (density-based spatial clustering of applications with noise) to identify the obstacle and calculate of the collision time according to the 6 sigma principle. Based on this algorithm, a pre-collision system was developed and integrated with agricultural machinery navigation software. The harvester was refitted electronically, and the system was tested on a harvester. The results showed that the system had an average accuracy rate of 96.67% and an average recall rate of 97.14% for being able to stop safely for obstacles in the area of interest, with a summed average of 97% for both the accuracy and recall rates. The system can be used for an emergency stop when encountering obstacles in the automatic driving of agricultural machinery and provides a basis for the unmanned driving of agricultural machinery in more complex scenarios.

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