Research on Distributed Task Allocation Algorithm for UAV Swarm Based on Evolutionary Game

With the advancements in technologies such as artificial intelligence, communication, and microelectronics, unmanned systems have rapidly developed and gradually replaced humans in certain challenging and adversarial domains, taking on increasingly complex tasks. Constrained by energy supply, unmanned systems, represented by UAVs, have limited payload, computing power, and autonomy. Faced with intricate tasks in high-dynamic environments, people address the challenges by scaling up unmanned systems to achieve victory through quantity. When employing massive unmanned swarms to execute complex missions, a primary concern is how to efficiently and reasonably allocate tasks. This paper, grounded in evolutionary game theory, proposes a novel method for task allocation in UAV swarms. Each UAV evaluates the task completion of neighboring UAVs and adjusts local tasks based on replicator dynamics. Through multiple iterations, the proposed approach achieves global optimal task allocation. Finally, the effectiveness of the algorithm proposed in this paper is validated through simulations. The research findings presented herein can provide support for distributed task allocation in massive unmanned swarms under conditions of limited computing power and restricted communication.