# Research Progress on Synergistic Technologies of Agricultural Multi-Robots
## 3 Research Progress on Synergistic Technologies of Agricultural Multi-Robots
### Path Planning  
Path planning is the fundamental guarantee for multiple robots to accomplish tasks together. This technology refers to using the known static environment information, or the dynamic environment information obtained by the sensor, to autonomously plan a collision-free optimal path for each robot from a known starting point to a target point, which requires not only a single robot to avoid obstacle but also a plan to satisfy collision avoidance among multiple robots [93]. The path planning methods for single robots to avoid obstacles mainly include traditional methods, intelligent methods, and other methods. The traditional methods include the construction space method, V-Graphic (visibility graph), Voronoi diagram, grid method, A* algorithm, and artificial potential field [94]. Intelligent methods include the ant colony algorithm, particle swarm algorithm (PSO), reinforcement learning algorithm, immune algorithm, genetic algorithm (GA), neural network, and fuzzy logic algorithm [95]. Other methods include dynamic programming (DP) and optimal control algorithms. Collision avoidance strategies among multiple robots include the priority method, rate adjustment method, traffic management rule, and consultation method. The path planning method can be divided into centralized path planning and distributed path planning according to the ability classification of path planning [96]. * Centralized path planning  
The centralized path planning method uses a centralized control unit to plan the optimal path for swarm robots. This method can improve the ability of \"close coordination and optimal coordination\" among mobile robots [97]. However, it encounters other problems such as \"dimensions\", \"computational complexity\", and \"non-deterministic polynomial problem (NP) difficulties\" with the increase in the number of robots, task difficulty, and space complexity. In particular, the \"NP difficulties\" problem [98; 99], in theory, has not yet received a simple or fast solution. Figure 10: Heterogeneous sensors carried by agricultural multi-robots. (**a**) Typical sensors for aerial robots: inertial measurement unit (IMU), Global Positioning System (GPS), and pressure gauge [28]. (**b**) Typical sensor for ground robots RT1 and RT2: GPS, Laser, and IMU [45]. Connecting known path points into a line, which is simulated to a topological model, and letting multiple robots walk along their respective paths is one of the simplest and easiest path planning methods to implement. But, this method requires obtaining accurate known point information in advance, which is a large preliminary job and is not suitable for situations with a large number of robots or a large operating area. A method similar to the point-to-point method is the visible map method, which aims to reduce collisions. That is, the information of the edge projection points of each obstacle is obtained in advance, and the robot free walking path, path points that can be combined or disconnected are represented by edges and nodes, respectively. Then the starting node is connected to the target node, or the starting node is connected to the raised point of each obstacle edge until it reaches the target node, forming a multi-robot walking path. Finally, depending on the size of the robot, the path width is increased or decreased appropriately. Similarly, the workload of measuring points is larger and does not apply when there are more obstacles. Planning multi-robot paths on mapping is another method of global path planning. For example, in the grid method, the map was divided into multiple grid cells, and the paths were extended in eight directions with each grid cell as the center, and the path segments were formed by connecting the center of the grid vertically and diagonally with the centers of other grid cells.