# Research Progress on Synergistic Technologies of Agricultural Multi-Robots
## 3 Research Progress on Synergistic Technologies of Agricultural Multi-Robots
### Path Planning
Figure 11: The central controller plans the driving path for the unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs) [49]: **(a)** The path planning of two UAVs; **(b)** The path planning of three UGVs. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Planning** & **Environmental** & **Head-Turning** & **Coordination** & **Obstacle** & **Application** \\
**Method** & **Circumstances** & **Mode** & **Strategy** & **Obstacle** & **Application** \\ \hline \multirow{4}{*}{\begin{tabular}{c} Point-to-point \\ planner \\ \end{tabular} } & \multirow{4}{*}{Fully known} & \multirow{4}{*}{Right-angle turn;} & \multirow{4}{*}{None} & \multirow{4}{*}{None} & \multirow{4}{*}{None} \\  & & & & & \\ \cline{3-5}  & & & & & \\ \cline{3-5}  & & & & & (simulation) [107] \\ \hline \multirow{4}{*}{\begin{tabular}{c} Visibility graph \\ (V-Graphic) \\ \end{tabular} } & \multirow{4}{*}{Fully known} & Zigzag; & Priority; & \multirow{4}{*}{Vehicle} \\  & & U-shape; & & & & \\ \cline{3-5}  & & Right-angle turn & & & & \\ \hline \multirow{4}{*}{Grid method} & Partially known & Zigzag; & Others & Pole & Seeded in a field [58] \\  & Fully known & U-shape; & Rate adjustment & Human & Harvested muddy moss in the \\ \cline{1-1} \cline{2-5}  & & & & & & \\ \cline{1-1} \cline{2-5}  & & & & & & \\ \cline{1-1} \cline{2-5}  & & & & & & \\ \hline A * algorithm 1  & Partially known & None & Others & None & Monitored in a vineyard [28] \\ \hline \multirow{2}{*}{\
\begin{tabular}{c} Beadth-first \\ search algorithm (BFS) \\ \end{tabular} } & \multirow{2}{*}{Partially known} & \multirow{2}{*}{None} & \multirow{2}{*}{Others} & \multirow{2}{*}{None} & \multirow{2}{*}{Weeded in a rice field [109]} \\  & & & & & \\ \cline{1-1} \cline{2-5}  & & & & & \\ \hline \multirow{2}{*}{
\begin{tabular}{c} Genetic \\ algorithm (GA) \\ \end{tabular} } & \multirow{2}{*}{Partially known} & U-shape & \multirow{2}{*}{Rate adjustment} & \multirow{2}{*}{None} & \multirow{2}{*}{Weeded in farmland [101]} \\  & & The light bulb shape & & & \\ \hline Others & Fully known & Right-angle turn; & Rate adjustment & None & Drove in a greenhouse [56] \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Research progress of path planning method for agricultural multi-robots. Combining the above path planning techniques, we summarize the research progress of agricultural multi-robots in path planning methods over the past 10 years, as shown in Table 5.  
It can be seen from Table 5 that agricultural mobile robots mainly use ground robots in farmland, and the number of robots is no more than four. The path planning method of multi-robots is mainly conducted in fully known conditions, and the grid method and V-Graphic under centralized planning are the methods most commonly used. The collision avoidance strategy between agriculture robots usually does not incorporate changes in the path [110], and the obstacle avoidance strategy of a single robot comprises mainly speed adjustment and the priority principle. The path planning of multi-robot generally does not consider the presence of obstacles in agricultural production. When the UGVs turn around at the headland, they need to consider the relationship between the minimum turning radius and the headspace. The general head-turning method has a bulb shape (as shown in Figure 10(b), zigzag or U-shape, etc. The U-shape predominates in practice. As shown in Figure 13, the zigzag (forward-reverse-forward) is used in smaller spaces; in contrast, the U-shape turn (turn-straight- turn) is used in larger spaces. However, the difficulty of robot control is increased with the zigzag turning shape, and the task allocation and path planning of the multi-robot are prepared for U-shape turning in advance. Figure 12: Distributed path planning [28], 1, 2, and 3 are the number of the UAVs, the red line is the planned path of the robot in the delimited area, and the blue line is the actual flight path of the robot.