# Designing UAV Swarm Experiments: A Simulator Selection and Experiment Design Process
## 5 Experiment Descriptions
ES4 experiments focused on swarm topology and inter-agent policies discussed above. Formation controllers can be further fine-tuned to adjust the allowable intrusion into an agent's sphere of minimum distance. Low tolerance controllers record even the least amount of intrusion by one agent into another agent. These incidents are then noted by swarm control and corrective action for agent movement is taken. There is a fine tradeoff between the set tolerance and the computation power required. Low tolerance controllers promise lower collision rates; however, even minimum intrusions are examined. This results in more computational power used to examine intrusions and take corrective action. Higher tolerance controllers may not realize all intrusions except the most severe, freeing up computation power but opening up the risk for unpredictable collisions. Inaccuracies in measurement can be supplemented by additional methods. This can work for any sensor reading including GNSS (Global Navigation Satellite System) readings. A GNSS positioning system can be supplemented with localized passive beacons that transmit exact information, whereas drifts in agent movement can be compensated using formation control and additional sensors. Figure 12 shows this simulated approach. Figure 11: MATLAB simulation using the UAV Toolbox sub-figures on top show different angles of the simulated UAV and the bottom shows the feed from the fisheye image sensor. Figure 12: A 3-agent swarm simulated in MATLAB with a downward sensor for passive beacons and a limited-range lateral sensor to detect other agents. Figures 13 and 14 show three and fourteen agents in a swarm, respectively. A minimum and maximum distance between agents are set as means to complement other sensors and swarm formation methods. Based on the tolerance level set and the level of intrusion by one agent into the space of another, the controller alerts the possibility of an inter-agent collision such that suitable adjustments can be made to avoid it. An ES5 experiment has been designed in Webots for examining the application-specific functionality of a homogeneous swarm in a realistic environment. Here a swarm of low-level UAVs indicated by the green oval in Figure 15 work to locate a person in distress. An external agent controlled by an operator is also shown to provide an additional POV (Point Of View). Floating windows show an agent POV that has located the person in distress, a second POV from a different agent in the swarm, and the view of the manually controlled M  
Figure 14: Fourteen agents flying the airspace with maximum and minimum inter-agent distances set for formation control. Figure 13: Three agents in an airspace with maximum and minimum inter-agent distances are set for formation control. An additional pass-on module to MATLAB tracked agent positions to collect and visualize position data as a means of creating an efficient distance to network strength formation controller for maintaining inter-agent distances. The developed approach uses the concept of proportional changes in inter-agent network strengths as a result of changes in distance between them. APF (Artificial Potential Field) is used to create attraction and repulsion forces. The figure shows the Crazieflie [78] agents in a swarm converging on the person. Target search and detection applications such as this open a range of possibilities in disaster management and emergency response [5; 85; 86]. The inclusion of agents with different capabilities in swarms is necessary to push the limitations of performance set by homogeneous swarms. A heterogeneous swarm can be recognized by various factors such as different operational spaces the swarm agents work in, the different nature of agents, or their hardware.