Biomimetic Multi-UAV Swarm Exploration With U2U Communications Under Resource Constraints

Existing multi-agent algorithms struggle with spatial coordination and require extensive prior environmental information for effective large-scale operation. This study introduces an innovative multi-UAV system that employs an evolutionary algorithm, reconceptualizing collaborative search tasks as a multi-objective cooperative challenge. Inspired by bacteria's stochastic exploration for pheromones, the algorithm utilizes pheromone concentration and gradient effects to enhance multi-source exploration's efficiency and robustness. A bespoke multi-UAV framework with UAV-to-UAV (U2U) communication was developed to implement and test the algorithm. Comprehensive experiments tested various chemotaxis strategies, guidance strengths, team sizes, and starting conditions, demonstrating the algorithm's consistent effectiveness in expansive terrains without relying on prior information.