A Bioinspired Adaptation-Based Algorithm for Swarm of UAVs in multiple threat prone Environments

In this paper, a biologically inspired diffusion adaptation-based algorithm is implemented for a swarm of UAVs. The swarm of UAVs in pursuit of food forage to a desired target in a cooperative manner. In the event of a predator attack, the swarm is capable of evading it using the adapt and then combine algorithms. The navigation algorithms use optimal fractional order PID (OFOPID) and are designed in a way to allow the UAVs to swiftly switch to the desired mode of foraging and evasion based on the environmental conditions. To approximate the environment with a real-world scenario, we added unstructured obstacles such as buildings or trees, which the agents avoid colliding, during the navigation. Further wind turbulence zone is created that emulate a storm or typhoon in the real world to increase the complexity of the environment. The fleet of UAVs is capable of evading the attack using a cognitive evasion algorithm. Simulations is performed using Python, and the results exhibit the effectiveness of the work.