# A New Mobility Model for Multi-UAVs Reconnaissance Based on Partitioned Zone
## 2 Related Work and Research Motivation  
### Related Work  
The path planning-based cooperative reconnaissance decides UAVs' paths in advance in order to reconnoiter a given area [13]. A central planner or controller configures the area and path for each UAV to cover. Let us assume that four UAVs are to reconnoiter a square area. Then, one simple path planning is to command each UAV to cover a quarter of an area evenly. In [18], the authors studied a cooperative path planning with multiple UAVs in an irregular polygonal area. The concave and convex area is partitioned into the same number as UAVs. The UAVs perform searching mission in their own allocated area. When a UAV malfunctiones, the area assigned to each UAV is reconfigured. In [19; 20; 21; 22], the authors studied optimization problems of path planning for the purpose of avoiding some threats. If there is a threatening spot on a planning path by the operator, the UAV avoids it and returns to its original path. On the other hand, if there are no threatening spots, the UAV moves to the path planned by the operator. The algorithms for avoiding threatening spots in path planning method have used Particle Swarm Optimization [20], Ant Colony Optimization [21], Artificial Bee Colony algorithm [22], etc. This central path-planning method is efficient in terms of coverage rate. However, the method cannot cope with unexpected events such as operational failure of UAVs, dynamic movement of targets, etc. Another weakness of the method is high predictability of reconnaissance path, which enables opponents or targets to avoid the reconnaissance. In addition, as the number of UAVs is increased, it is not a trivial problem to configure the path of each UAV optimally with consideration of many uncertain events. Another approach of cooperative reconnaissance is the random mobility method in which UAVs reconnoiter without prior knowledge. The random mobility method depends on the mobility model of individual UAV movement. Thus, many recent studies on dynamic reconnaissance of multiple UAVs used mobility models developed in FANET (Flying Ad-hoc Network) or MANET (Mobile Ad-hoc Network) in order to model the UAV movement. Mobility models can be defined as movement of nodes over time and provide an environment for realistic simulation [23]. In FANET, mobility models are used to generate flight trajectories and evaluate the performance of FANET protocols [24]. To perform the mission of reconnaissance, the Random Waypoint model and the Random Walk models have been used as basic models due to their simplicity [12; 25; 26]. As shown in Figure 1a, each mobile UAV in Random Waypoint model selects a destination randomly in the area and then moves toward the destination at a speed which is also chosen randomly between 0 and \(V_{max}\). In the Random Walk model, each UAV randomly and uniformly chooses its new direction between 0 and 360 degrees, and the new speed between 0 and \(V_{max}\). Then, they move for some time interval between 0 and \(T_{max}\). While the Random Waypoint model in MANET considers pause time at the destination, the model in FANET does not consider the pause time. Therefore, the two models, i.e. the Random Waypoint model and the Random Walk model, have the same movement in FANET. In this paper, we regard the Random Waypoint model as the same model as the Random Walk model. Figure 1b shows Markov Process model, which is also used as a basic random mobility model for reconnaissance. This model randomly decides one of three directions at every unit time: turning left, flying straight, and turning right. The probabilities of directions are given differently depending on the current status of a UAV. For example, when a UAV flies straight, its next probability of flying straight is 80%, the probability of turning left is 10%, and that of turning right is 10%. Likewise, when a UAV turns left, its next probability of turning left is 90%, the probability of flying straight is 10%, and that of turning right is 0%.