Multi-AAVs Flocking for Navigation and Obstacle Avoidance in Network-Constrained Environments

The flocking movement is a fundamental and crucial operation in multi-AAVs systems, encompassing navigation and obstacle avoidance. However, traditional flocking algorithms typically rely on rigid rules and exhibit limited adaptability to diverse environments. Reinforcement learning (RL) effectively addresses this issue as a flexible and model-free framework. In this article, RL techniques are utilized to achieve navigation and obstacle avoidance for a swarm of AAVs. To enhance training efficiency, we propose an improved algorithm called heuristic guides TD3 (HGTD3) by integrating heuristic guides with the twin delayed deep deterministic policy gradient (TD3), aiming to address the protracted learning periods commonly observed in traditional RL methods. Considering the network-constrained environment, we propose the negative interference flocking algorithm (NIFA): the network interference flocking algorithm and an AAV flocking algorithm designed based on the sparrow search algorithm. NIFA can guide the losing AAV to follow the swarm and at the same time maintain the overall navigation and avoidance efficiency. Finally, we demonstrate the scalability and adaptability of HGTD3-NIFA in a simulation experiment in terms of multi-AAVs flocking and navigation.