# Research Progress on Synergistic Technologies of Agricultural Multi-Robots
## 3 Research Progress on Synergistic Technologies of Agricultural Multi-Robots
### Multi-Robot Task Allocation  
Multi-robot task allocation (MRTA) provides evaluation indicators of a multi-robot system, a task set, and system performance and finds a suitable robot for each subtask to perform, bringing the benefit of a robot system to perform mostly task collection. Thus, the quality of the MRTA results directly affects the efficiency of the entire system and whether each robot of the system can maximize its capabilities [78]. Solve the MRTA problem [79] involves many aspects, such as the capability attributes of the system members, the structural attributes of the tasks, the robot coordination mechanism, and the strategy of task allocation [80]. This approach divides the assignment of agricultural multi-robots into centralized components based on the decision of the central controller in this part. * Centralized Allocation  
The centralized allocation means that the leading robot or control center of the system decomposes the global task and then sends the decomposed subtask to each robot according to the corresponding allocation method. In 2012, reference [14] reported the work area division and task assignment of two UGVs by a person through remote monitoring based on a citrus orchard map. However, as the number of robots increased, manual task assignment stops when a robot hits a tree or fails to make a reliable turn. Some scholars [66] solved the manual assignment of tasks to multiple robots from the perspective of dividing maps, namely, as many sub-regions as there are robots needed. The edges of these sub-regions are generated from discrete fruit tree points and K-mean clustering points on the map boundary using an integer programming approach. However, this view is too ideal and does not consider the case where the number of robots is less or more than the total number of tasks. A similar study on the number of robots over the number of tasks is multiple robots and a small number of refueling stations [81]. In this reference, an approximate arbitrary time algorithm based on the branch delimitation method was used to obtain the task sequence of multi-robot collaborative refueling. The limit value of the path length distance was first calculated and used as the upper and lower bounds of the algorithm nodes based on the rules for robots walking infield and the total time cost function of refueling and waiting in the queue. Then the optimal solution was obtained by deleting the sub-nodes whose lower limits were greater than or equal to the optimal upper limits during iteration. Simulation experiments showed that the optimal approximate solution on resource utilization can be found by this method, but it is difficult to apply the method to other aspects of agriculture, such as spraying. A reinforcement learning-based method (Dyna-Q +) was used to find the optimal search path from the current point to the endpoint, that is, multiple robots randomly selected actions (front, back, left, right) and each action was recorded, and the optimal path was obtained by rewarding and punishing the actions selected by the robots according to the presence or absence of obstacles [62]. Then a weighted graph was used to represent the Gird model, including parameters such as the current position of the robot, the set of grids, and whether there is a path between the grids. The minimum cost time for the UAVs to fly at different speeds under the optimal path was calculated using [PERSON]'s algorithm, and finally, the search space was obtained based on the optimal path and time and allocated to multiple robots in proportion to the size of the space. A reinforcement learning [82] is a Markovian decision process where the basic idea is all about modeling or fitting a strategy using a function for more complex decision problems. However, the method requires a large number of samples and a long time when used. Also, multiple robots working in an agricultural environment are often subject to resource-sharing conflicts.