# Designing UAV Swarm Experiments: A Simulator Selection and Experiment Design Process
## 5 Experiment Descriptions
An example of a swarm with all three properties was simulated in a CoppeliaSim simulation in Figure 16. An agent with a close-range sensor for obstacle detection works with an agent that has a long-range vision sensor; a ground-based wheeled robot is also a part of the swarm, and a tree is included as a sample for a static obstacle and a walking person is a dynamic obstacle. The aerial agents work in conjunction to detect obstacles and share information across the swarm. The ground-based robot is assigned different functions depending on the scenario such as acting as a platform for the safe landing of aerial agents or finding safe spots for the agents to land on the ground. The green spheres indicate possible zones for movement as mapped by the ground robot. Efficient information exchange pathways and the demonstration of heterogeneous agents with extended parameters are one of the targets for ESS. Simulation quality is enhanced with features such as obstacle variety, realistic scenery, and a mixed-sensor array. To design and test varied systems such as this, it is necessary for the right choice of simulation platforms that can support it. Experiments beginning from ES3 use simulation platforms where it might be possible to introduce variability in experiments scenarios using weather effects. Rain, wind, and snow exert significant influence on UAV operations, underscoring the need for comprehensive testing. Rain can compromise UAV sensors and electronics, potentially disrupting communication and navigation systems. Wind poses challenges in maintaining stable flight paths and can increase energy consumption. Snow accumulation can alter UAV weight and aerodynamics, affecting maneuverability. The sun's azimuth angles collectively shape the complex interplay of factors affecting UAV operations. The sun's azimuth angle  
Figure 7:0 A distributed low-cost UAV swarm working towards targeted search and rescue simulated in a hyper-realistic environment in Webots. determines the direction of sunlight and casts shadows, impacting visual perception and sensor data accuracy. UAVs' ability to interpret their environment and execute tasks can be compromised by glare, changing illumination, and altered shadow patterns. Properly accounting for the sun's angle is crucial for accurate navigation, object recognition, and obstacle avoidance. All of these factors collectively impede control precision and reduce flight endurance, necessitating robust algorithms and hardware designs to ensure reliable UAV performance in adverse weather conditions. Integrating these environmental variables into UAV operation testing provides a comprehensive understanding of their combined impact, enabling the development of more robust and adaptable UAV systems capable of functioning effectively under a wide range of real-world conditions. Figure 17 shows an example of how the presence of fog and changes in the sun's angles may influence object detection by vision sensors. Adverse weather conditions may require the implementation of add-on methods to ensure that the output of sensors remain coherent [87]. The performance of other sensors such as LiDAR may also be affected. Table 5 summarizes some simulation platforms and the various weather effects that they are capable of supporting. Weather conditions such as wind, rain, and fog, can significantly impact the performance of UAVs, challenging their navigation, communication, and coordination capabilities. By incorporating weather effects, researchers can more accurately assess the resilience of swarm algorithms and control strategies under diverse environmental conditions. This approach not only provides a more comprehensive evaluation of UAV swarm behavior but also aids in identifying potential vulnerabilities and optimizing strategies for real-world deployment. Ultimately, the integration of weather variability lends a vital layer of complexity to UAV swarm experiments, promoting the development of more adaptable and robust swarm technologies.