An Adaptive Approach To UAV Interaction Via Swarm Intelligence
Unmanned aerial vehicles (UAVs) have the potential to be a key component in several sectors and applications as technology advances. Longer flying periods, better sensors, and enhanced control are anticipated along with a more complex design. Gupta (2023) further described that UAVs are beginning to be utilized for delivery, inspection, and surveillance, potentially in the future, they will also be employed for disaster response, monitoring of agriculture, and search and rescue missions. It is expected that UAV technology may improve in all conceivable areas, influencing the way individuals live in the future. A primary challenge facing UAVs is their limited flight endurance, which stems from the constraints imposed by batteries as their power supply. This results in some UAVs having limited flight time, autonomy, mobility, and battery endurance. Furthermore, harsh weather conditions and environments can further impede UAV performance. Consequently, mission time is restricted by low battery endurance, challenging atmospheric conditions, and sensor accuracy issues.
Researchers have worked with biomimicry, drawing inspiration from nature to improve UAVs, in order to address this problem. According to Ákos et al. (2010), the solution to this issue is to imitate soaring birds' actions, which serve as an illustration of how to use a thermal updraft to fly higher and stay in the air considerably longer. Researchers have been attempting to reproduce this behavior in UAVs recently by employing thermal sensors and analysis to find and take advantage of updrafts.
This research analyzes how a swarm of UAVs can work together to make optimal usage of thermal updrafts while also highlighting the variety of scientific concepts that has been observed. The project operates this by Analyzing UAV Thermal Soaring via Hawk-inspired Swarm Interaction. The movement of the UAVs was simulated using a Boids model, which imitates the behavior of birds in a flock by utilizing forces of cohesion, separation, and alignment, which indicate that it may significantly affect the endurance and range of UAVs while lowering their energy consumption. By integrating an adjusted behavioral model into the agents, they showed a dynamic flocking behavior that involved maintaining proximity based on their altitude, separation, and alignment. The simulation results demonstrated that the agents clustered together around thermal air currents, thereby enhancing their chances of survival. These results suggest that this method has the potential to increase the flight time of autonomous UAV swarms, offering an encouraging prospect for future research.
This study is an interdisciplinary research, mainly focusing on concepts found within the fields of physics and biology, although chemistry is also touched. This allows for a broader and more complex view of the problem, possibly leading to a better and simpler solution. These different fields of study all work together to generate the behavioral model that, theoretically, could improve the flight times of UAV swarms.
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Hawks are recognized for their efficient long-distance flight accomplished through thermal soaring, wherein they take advantage of rising columns of air known as thermals . By adjusting their flight path and speed, hawks stay within the thermal column, thereby gaining or maintaining altitude with minimal energy expenditure. The birds optimize their wing and body positions to maintain a constant angle of bank and pitch, maximizing the thermal's upward-moving air currents to remain aloft (McDonald, 2016). Thermal soaring conserves energy, which is especially beneficial during food-scarce winters or migratory periods when hawks require significant energy reserves for long flights (Addison, 2022).
This study shows insights on possible concepts and behaviors that can be applied to real world UAV agents. The agents showed a level of swarm intelligence that was powerful enough to not need any communication between the agents. This means that the model provided in the study is capable of being implemented in a system consisting of multiple agents that do not have any direct communication with each other. The study also showed that the more agents there were in a system, the better the swarm’s effectiveness. This means that the behavioral model improves if there are more agents available, meaning that UAV agents can be made using cheaper and less sophisticated technology in favor of this emergent swarm behavior. The study can also be applied to biology, specifically to the study of hawk behavior. Studying the model may provide insights on the emergent behaviors of a hawk swarm, as the model was itself inspired by hawks.
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