# Research Progress on Synergistic Technologies of Agricultural Multi-Robots
## 3 Research Progress on Synergistic Technologies of Agricultural Multi-Robots
### Path Planning
To get the globally optimal path in the grid, the A* algorithm was used to search for the path segment with the lowest travel cost, in which the cost of the free space cell was set to 0, the cost of the cell with obstacles was set to the maximum, and the travel cost was the sum of all grid cells on the travel path segment, and the globally optimal path was set when the sum was the smallest. Since information about the farmland changes dynamically, multiple robots operating with precision need to re-plan to create multiple paths each time based on different information. This multi-robot path planning problem with time windows has also been solved as a multi-objective optimization problem. As shown in Figure 11, where a multi-robot system including two aerial robots and three ground robots was jointly developed in Spain and other countries [49]. This system adopted a centralized control unit to provide global path planning for the multiple robots on a grid map with weed information, which divides the sequence of operations for the multiple robots in advance. The No dominated Sorting Genetic Algorithm II (NSGA-II) algorithm [100; 101] was then used to coordinate the relationship between the distance of the robot travel path, the number of turns, the number of robots, the amount of weed killer used, and the capacity function to obtain an approximate optimal solution between the time and money spent by the multiple robots and the cost of weed treatment. It is a type of genetic algorithm, which mainly focuses on the simulation of crossover, variation, and hereditary phenomena occurring during natural selection and genetic inheritance, incorporating the natural law of superiority and inferiority, and deriving the candidate solutions for each generation based on the results, and finally deriving the optimal solution from the derived candidate solutions. However, this method is more computationally intensive and the experimental results are not suitable for fields that are unstructured or d fields without a fixed column or row lengths. * Distributed path planning  
The distributed path planning method requires little calculation and is robust but exhibits low efficiency and can provide only a suboptimal solution [102; 103]. In a fully known environment, it is necessary to consider each robot obstacle avoidance method and collision avoidance strategy among robots [104], that is, selecting a robot for path planning first, then broadcasting its path to other robots, and finally planning paths of other robots by themselves. However, this method is difficult to achieve [97] for large numbers of robots. In an unknown environment, the preferred method is to plan a path for each robot to avoid static obstacles based on neglecting the movement of other mobile robots in the environment and then using the multi-robot collision avoidance strategy to solve the conflict problem among mobile robots. At present, there is little research on the distributed path planning of agricultural multi-robots. As shown in Figure 12, [PERSON] et al. [28] developed UAVs to monitor agricultural information in the vineyard. The path planning of the UAVs is based on the grid map and A*. On the map, the UAV path planning function is constructed according to constraint conditions, such as the number of UAV turns, the number of covered grid visits, and the time to complete the single partition. And using a heuristic algorithm like A*, the next best node to be expanded is obtained by partying the generation value of each node. Then, the breadth-first search (BFS) algorithm is used to find the local maximum of the function by the distance between the cells. The result is the path from any starting point in the environment to the target unit. The practice shows that the method can find the approximate optimal solution, reduce the possibility of repeated access to the same cell, and facilitate the avoidance of known obstacles. However, it needs to consider the local environmental conditions to find wide applicability.