# Research Progress on Synergistic Technologies of Agricultural Multi-Robots
## 3 Research Progress on Synergistic Technologies of Agricultural Multi-Robots
### Environment Perception
#### 3.2.1 Co-Location Technology
The above co-location methods are summarized as shown in Table 1.  
It can be seen from Table 1 that the cooperative localization technology for agricultural multi-robots mainly adopts active localization and passive localization, which are computationally small and easy to implement. The real concept of \"co-location\" should be interactive positioning; through communication among robots, information sharing can be realized, and then the robot's positioning error can be corrected to achieve accurate positioning. However, this method incurs a large amount of calculation and a large complexity of the algorithm, making it difficult to implement. No examples have been found in the literature for agricultural multi-robots. More practical applications indicate that the cooperative positioning of multiple robots in agriculture is replaced by a central controller or task manager coordination mechanism [49; 50]. That is, the working area and travel path of each robot are planned by a task manager or central controller based on the established environment map. The work areas and work paths of these multiple robots usually do not intersect and each robot only needs to localize and navigate based on its sensors. For example [27], the vineyard area was divided into three UAV-monitored vineyard areas using the task manager according to set rules, and path planning was performed for the sub-areas of individual UAV operations, with each UAV flying at a different altitude to avoid collisions between the UAVs. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Co-Location Methods** & **Active Positioning** & **Passive Positioning** & **Interactive Positioning [41]** \\ \hline Data measurement object & Robot itself & Neighbors & The robot itself; Neighbors \\ \hline Observation reference object & Neighbors & Robot itself & The robot itself; Neighbors \\ \hline Measuring object & Single & Single & Mutual observation \\ \hline Measurement data & The distance and angle of the robot itself relative to the reference object \\ \hline Data exchange & None & Exists & Exists \\ \hline Data Fusion & None & None & Exists \\ \hline Advantages & Little computation & Information interaction & Algorithm of high precision and robustness \\ \hline Disadvantages & \begin{tabular}{c} Affected by the environment \\ Lower algorithm accuracy \\ and robustness \\ \end{tabular} & \begin{tabular}{c} Affected by the environment \\ Lower algorithm accuracy \\ and robustness \\ \end{tabular} & \begin{tabular}{c} A huge amount of information \\ Lower algorithm accuracy \\ and robustness \\ \end{tabular} & \begin{tabular}{c} Large complexity of \\ the algorithm \\ \end{tabular} \\ \hline Applications & \begin{tabular}{c} Drove on farmland [42] \\ \end{tabular} & \
\begin{tabular}{c} Drove on sloping land (11\({}^{\circ}\)) [43] \\ \end{tabular} &
\begin{tabular}{c} \\ Plowing [44] \\ \end{tabular} & None \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of co-location methods.