# Research Progress on Synergistic Technologies of Agricultural Multi-Robots
## 3 Research Progress on Synergistic Technologies of Agricultural Multi-Robots
### Formation Control  
Multi-robot formation control technology means that multiple robots maintain a certain formation to the target while still adapting to environmental constraints such as obstacle blocking or spatial physical limitations [111]. This technology can improve the robustness of the multi-robot system, and the robots can complete the task with higher efficiency and shorter time [112]. At present, the formation control technology of agricultural multi-robots is divided into forming formation and formation control. * Formation forming  
As shown in Figure 14, the formation of agricultural multi-robots generally has five types: column, I-shape, linear, V-shape, and W-shape, and the circular nodes in the formation structure represent robots. Each robot is represented by RID, such as R1 and R2, and the black arrow indicates the direction of robot movement. It is necessary to achieve the desired formation by determining the formation position reference point after determining the root formation. There are usually three reference points: center, neighbors, and leading robot; as shown in Figure 15, the position of each root node is represented by PID, such as P1 and P2, and the arrows indicate the relationship between robot dependence and information transfer. * Formation control  
From the perspective of a multi-robot system control framework, formation control is divided into two types: centralized control and distributed control. The former uses a centralized control unit to make decisions, optimize robot coordination, accommodate individual robot failures, and supervise the entire group of robots. The latter does not have a unified control unit, and a single robot makes decisions based on its local information [112; 113]. At present, the method of centralized formation control of agricultural multi-robots includes the virtual structure, graph-theoretic approach [114], and model predictive control [115; 116]. The method of distributing the formation control of agricultural multi-robots includes leader-follower [34; 41; 75; 117] and the artificial potential field [113]. Figure 14: Multi-robot team arrangement, the arrow points to the direction of robot movement, and the circle represents the robot: (**a**) The robots of R1 to R5 formed a longitudinal linear queue. (**b**) The robots of R1 to R5 formed an I-shape queue. (**c**) The robots of R1 to R5 formed a transverse linear queue. (**d**) The robots of R1 to R5 formed a W-shape queue (**e**) The robots of R1 to R5 formed a V-shape queue. [PERSON] et al. in France [44] adopted the bidirectional control strategy based on the virtual structure method. As shown in Figure 16a, each robot of the whole queue is a fixed point on the virtual structure. In this structure, the queue also increases two virtual leaders' interaction with the extreme robots and carries the desired global velocity for the whole fleet. The advantage of this method is simpler communication protocols and lower communication costs; however, the reaction of the robots is slower because of different acceleration performances. [PERSON] et al. in the USA adopted the graph theory approach in bee pollination [108]. When a beehive was opened, the swarm robot flew radially from a moving beacon at a constant speed. And once it encountered the edge of the graph, it flew eastward at a fixed speed. As the robot flies over the plant, it acquires at least one flower within its range through sensors and hovers over the flower with unit time probability to pollinate it and record the location of the pollinated flower, returning to the hive after pollination and starting the next flight until complete coverage of the whole field is achieved. However, this method takes a long time and the model used in the simulation is too idealized. Whether they can be used for practical production needs to be further explored. [PERSON] et al. in Korea adopted model predictive control (MPC) and nonlinear feedback control respectively in fish tracking (simulation) [117].