The held is converted into a weighted directed graph *G* ( *V,E,C).* To obtain the graph, we define nodes as parts of the environment. The considered scenario is based on the methodology first proposed in [10]. To obtain an insight into the underlying process, let us consider a map of agricultural held, as shown in **Figure la**. The required information to model the presented held in **Figure 1a** as a graph is shown in **Figure 1b**. Each of the regions next to the plant rows, represented as a hachured rectangular, is considered to be a cell and is associated with a node in *V*. The black arrows indicate the allowed motion in each region and constrain the direction of movement of the robots. If a robot can move from one node to another, we assume that these nodes are neighbors and add a corresponding edge to *E* . Given the graph *G* , a cost (weight) between two neighbor nodes *c* (*x, y* ) ∊ *C, x, y*∊*V* means that, to go from node *x* to *y,* a robot must execute a command *I(x,y)* (such as Go-Straight, Turn-Right or Turn-Left) that will result in a cost *c (x,y).* In this particular example, for going from one node to another node, one of the following two commands should be executed by the robots, namely: **Command 1**: Go-Straight, and **Command 2**: Turn-Right (-Left), Go-Straight, Turn-Right (- Left). It is assumed that the cost values (weights) *one* and *three* result from Commands 1 and 2, respectively. Moreover, the black square and circle denote, respectively, the docking station and access point for recharging the robots. Based on the above description, the weighted directed graph corresponding to **Figure 1b** is shown in **Figure 1c**.