Biomimetic route planning for micro-UAVs using honeybee- inspired optimization

A biomimetic route optimization framework for micro-UAV systems is proposed in this work, which is inspired by the foraging process of honeybees (Apis mellifera) and employs AI. The research is field documented, expert interviews were held, and in combination with supporting microscopic evidence on the morphology of honeybees. The hive entry-exit processes, weather parameters, activity intensity and the environment were registered at three observation points in Goygol and Dashkasan regions of Azerbaijan at 10-minute intervals in the field. These data were analyzed as behavioral indices of adaptive resource search, route choice and group organization. Expert interviews about the behavior of honeybees revealed that honeybees select feeding sites based on distance, availability of nectar/pollen, odor, sight, and energy conservation; and that the information about promising feeding sites is transmitted within the colony through their movement. At the same time, the scientific community of artificial intelligence (AI) confirmed the validity of the use of swarm intelligence and bee colony modelling for routing, resource allocation and real-time adaptation problems in complex systems. From this the paper suggests a conceptual model to translate the searching logic of the honeybee to micro-UAV route optimization idea, such as decentralized search, adaptive path selection, exploration-exploitation balance and environmental responsiveness. Biomimetic evidence is provided by the presence of compound eyes and wing structures, in the form of the microscope images, to the concepts of multi-sensor perception and lightweight mobility. The results indicate that the bioinspired AI models can help to improve the energy efficiency, adaptiveness, and resilience of micro-UAVs, especially in the fields of environmental monitoring, search and rescue missions, and distributed observation systems.