Carrier platform-enhanced multiple-UAV cooperative task assignment with dual heterogeneities
Yu Xinyong, Xin Li, Lei Wang, Jin Junhong, Zhang Genlai, Xichao Su, Laifa Tao, Chen Lü, Wang Xinwei
Abstract
Heterogeneous unmanned aerial vehicle (UAV) cooperation has been widely used in modern warfare. Due to the limited UAV flight endurance, the operational range is generally constrained. This issue can be effectively addressed by utilizing various airborne or shipborne carrier platforms (CPs) such as large transporters and aircraft carriers. However, such a topic is rarely studied in existing research. This paper studies the carrier platform-enhanced multiple-UAV cooperative task assignment (CPMCTA) with dual heterogeneities (i.e., in both UAVs and CPs). Additionally, the approaching unattacked target-induced risk (AUTIR), which isneglected in traditional research, is also considered to improve the task implementation safety. A novel CPMCTA model with comprehensive factors (i.e., priority, obstacles, AUTIR and heterogeneities) is first established. Aiming at an efficient solution, an adaptive self-motivated teaching-learning-based optimization algorithm (AMTLBO) is then developed by integrating various mechanisms (i.e., multiple teachers, adaptive learning rate and self-motivation). Simulations under various scenarios demonstrate the advantages of the AMTLBO in optimum-seeking capability over the other six state-of-the-art algorithms. Moreover, the necessity of considering AUTIR is highlighted. A simulation animation is available at bilibili.com/video/BV1Ht421A7Qx to provide a clearer illustration.