# Designing UAV Swarm Experiments: A Simulator Selection and Experiment Design Process
## 3 Generalized Swarm Process Consideration during Development
### Deployment Strategies  
Deployment strategies [62] play a critical role in the effective utilization of UAV swarms over an ROI (Region of Interest). The choice of strategy depends on various factors, including the mission objectives, environmental conditions, available resources, and desired outcomes [63]. One commonly employed strategy is the grid-based deployment approach, where the ROI is divided into a grid pattern and UAVs are strategically positioned at predefined grid points. While the decomposed grid may be any polygon [64], four-sided cells are the most common. A collection of such cells is called a subgrid [65]. Depending on a use case scenario these subgrids may be of different shapes and dimensions. Grid-based decomposition ensures comprehensive coverage of the entire area and facilitates systematic exploration of the ROI. Deploying agents to assigned grids or subgrids is particularly useful in scenarios that require even distribution of surveillance or data collection efforts, such as monitoring large agricultural fields or conducting wide-scale search and rescue operations. Figure 2: Centralized topology (**left**) and decentralized topology (**right**) for a swarm of UAV agents. Another deployment strategy is targeted deployment, which involves placing UAVs at specific locations within the ROI based on the mission requirements. This strategy allows for a more focused approach, concentrating the swarm's resources on critical areas of interest. For instance, in disaster management scenarios, UAVs can be deployed near disaster-stricken regions or potential danger zones to gather detailed information, assess damage, or provide real-time situational awareness to aid response teams in their decision-making process. Figure 3 shows examples of deployment strategies that were visualized during the experiment design process. Predefined agent deployment assumes the initial placement of agents at selected points in the ROI; randomized deployment uses a single take-off point and is then refined on a randomized search by agents backed by consensus for target detection. On the left, the different colors indicate that each agent was preassigned its subgrid to search. On the right, randomized deployment shows all agents flying from a single take-off point, with no pre-assignment of subgrids. Figure 4 shows the two possible subgrid selections highlighted in yellow and green. These are just examples. In practice, the size and shape can be defined by the operator depending on factors such as agent capability, sensor range, coverage, or ROI geographical features. These subgrids can be selected initially and stay constant or may change dynamically across the mission timeline. Figure 4: Possible subgrid selections are shown as A or B.  
Figure 3: Predefined agent deployment (**left**); single point deployment (**right**). Randomized deployment strategies introduce variability and unpredictability into the swarm's positions. By using algorithms that generate random or pseudo-random deployment patterns, the swarm can explore the ROI in a non-deterministic manner. This strategy can be beneficial in scenarios where it is essential to reduce predictability, enhance resilience, or counteract potential adversarial efforts to anticipate the swarm's behavior. Randomized deployment strategies can be particularly useful in surveillance and security applications, where diverse and unpredictable movement patterns can make it more challenging for adversaries to identify and track the swarm's activities. In certain dynamic scenarios, adaptive deployment strategies come into play. These strategies involve dynamically adjusting the swarm's configuration based on real-time information and changing mission requirements. By leveraging feedback from sensors, communication networks, or external data sources, the swarm can adapt its deployment pattern, alter its flight paths, or redistribute resources [66] within the ROI.