Research on Cooperative Obstacle Avoidance Decision Making of Unmanned Aerial Vehicle Swarms in Complex Environments under End-Edge-Cloud Collaboration Model

Abstract

Obstacle avoidance in UAV swarms is crucial for ensuring the stability and safety of cluster flights. However, traditional methods of swarm obstacle avoidance often fail to meet the requirements of frequent spatiotemporal dynamic changes in UAV swarms, especially in complex environments such as forest firefighting, mine monitoring, and earthquake disaster relief. Consequently, the trained obstacle avoidance strategy differs from the expected or optimal obstacle avoidance scheme, leading to decision bias. To solve this problem, this paper proposes a method of UAV swarm obstacle avoidance decision making based on the end-edge-cloud collaboration model. In this method, the UAV swarm generates training data through environmental interaction. Sparse rewards are converted into dense rewards, considering the complex environmental state information and limited resources, and the actions of the UAVs are evaluated according to the reward values, to accurately assess the advantages and disadvantages of each agent’s actions. Finally, the training data and evaluation signals are utilized to optimize the parameters of the neural network through strategy-updating operations, aiming to improve the decision-making strategy. The experimental results demonstrate that the UAV swarm obstacle avoidance method proposed in this paper exhibits high obstacle avoidance efficiency, swarm stability, and completeness compared to other obstacle avoidance methods.