# Research Progress on Synergistic Technologies of Agricultural Multi-Robots
## 3 Research Progress on Synergistic Technologies of Agricultural Multi-Robots
### Environment Perception
#### 3.2.3 Mapping
However, the drawback of this mapping is a tradeoff between acquiring an equally sized set of images in cells and inefficient aerial sampling with UAVs. Geometric model  
Geometric features such as line segments or curves are extracted from the environmental perception information and used by robots to construct an environmental map [22]. As shown in Figure 7, [PERSON] et al. [65] defined the boundary of a 55-hectare sorghum stubble field by manually selecting appropriate latitude and longitude coordinates but omitted the headland where the UGVs turned around at the end of each row. Besides, the positions of multiple robots and obstacle information (human, pole, vehicle, etc.) were periodically sent to the central controller through sensors of a real robot and 12 simulated robots. Finally, the central controller constructed a 2D map of the environment surrounding the robot. A more scientific approach than manually labeling is the Voronoi diagram [66]. For example, a fruit tree on the robot's driving path was taken as a point in the orchard map, and the two-dimensional coordinates of this point were found in the image acquired by the UAV. At the same time, the data of planting interval and density of fruit trees were learned from this image, and the two-dimensional coordinates of other fruit trees were obtained, and then the K-means clustering algorithm was used to find the center of clusters of multiple fruit trees, and the orchard was divided into multiple sub-regions for robot operation according to the principle that the fruit tree point is most adjacent to this center. Figure 6: Grid model with rasterized areas [28]. Figure 7: Geometric models were constructed with points and lines [65]. But the experimental results after mapping are to be further validated on a real agricultural spraying multi-robot. * Topological model  
The topological relationship among objects in the environment is represented by the environment model [21]. The topological model consists of multiple unrelated points and lines with a simple model. Some researchers proposed to use this model in agricultural multi-robot path planning, but it is only an idea and has not been applied [67]. The specific application scope and characteristics of the multi-robot environmental model are shown in Table 3.  
Compared with 2D maps [14; 73; 74; 75], 3D maps can provide more information. By fusing the original measurement or small local maps generated from multiple robots to construct global maps at the same time, or matching the 3D maps constructed by heterogeneous robots, more and more abundant data are obtained, which is a new research direction of mapping. For example [71], the point clouds of the two images acquired by the UAVs and UGVs respectively were first represented by a grid model, and each cell stored excess vegetation index information and ground height information. After that, the relative displacement and rotation parameters between the two grid maps were extracted using the data provided by GPS and Attitude and Heading Reference Systems (AHRS), and the maps were matched by these parameters. Then the correspondence of point clouds between the matched maps was calculated using the Large Displacement Dense Optical Flow (LDOF) system and a voting scheme was used to select a coherent correspondence dataset with high data similarity to infer the initial conversion relationship between the maps, and a non-rigid alignment algorithm was used to eliminate orientation errors. Finally, the input point clouds were  
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brought in for a point-to-point alignment, and non-vegetation points were removed to obtain the 3D maps. The experimental results showed that the constructed maps matched well, but the time taken to construct each such image was quite long. As the farming environment is often dynamic, learning specific farming information while shortening the time to build maps will help improve the efficiency of multi-robot operations, which should be a direction for further research.