In recent years, with the development of the unmanned aerial vehicle (UAV) and battlefield environments, the UAV swarm has attracted significant research attention. To solve problems regarding poor state consensus among swarm individuals due to a small number of individuals easily falling into local minima upon encountering an obstacle, this paper proposes a flocking obstacle avoidance algorithm with local interaction of obstacle information. To make the UAV swarm follow the desired trajectory with better state consensus, we improved the flocking control algorithm of agents according to the characteristics and requirements of the UAV swarm. The obstacle avoidance algorithm for the UAV swarm is based on Olfati-Saber's multi-agent obstacle avoidance algorithm. The proposed method has individuals in the swarm communicate obstacle information with their neighbors, and we present a simple analysis of this method. The method improves the cooperative obstacle avoidance capability of the flocking control algorithm. The simulation results showed that the proposed flocking control algorithm provides a better tracking effect and consensus for the UAV swarm when avoiding obstacles.

Multiple unmanned aerial vehicle (UAV) collaboration has great potential. To increase the intelligence and environmental adaptability of multi-UAV control, we study the application of deep reinforcement learning algorithms in the field of multi-UAV cooperative control. Aiming at the problem of a non-stationary environment caused by the change of learning agent strategy in reinforcement learning in a multi-agent environment, the paper presents an improved multiagent reinforcement learning algorithm—the multiagent joint proximal policy optimization (MAJPPO) algorithm with the centralized learning and decentralized execution. This algorithm uses the moving window averaging method to make each agent obtain a centralized state value function, so that the agents can achieve better collaboration. The improved algorithm enhances the collaboration and increases the sum of reward values obtained by the multiagent system. To evaluate the performance of the algorithm, we use the MAJPPO algorithm to complete the task of multi-UAV formation and the crossing of multiple-obstacle environments. To simplify the control complexity of the UAV, we use the six-degree of freedom and 12-state equations of the dynamics model of the UAV with an attitude control loop. The experimental results show that the MAJPPO algorithm has better performance and better environmental adaptability.