  In unseen and complex outdoor environments, collision avoidance navigation
for unmanned aerial vehicle (UAV) swarms presents a challenging problem. It
requires UAVs to navigate through various obstacles and complex backgrounds.
Existing collision avoidance navigation methods based on deep reinforcement
learning show promising performance but suffer from poor generalization
abilities, resulting in performance degradation in unseen environments. To
address this issue, we investigate the cause of weak generalization ability in
DRL and propose a novel causal feature selection module. This module can be
integrated into the policy network and effectively filters out non-causal
factors in representations, thereby reducing the influence of spurious
correlations between non-causal factors and action predictions. Experimental
results demonstrate that our proposed method can achieve robust navigation
performance and effective collision avoidance especially in scenarios with
unseen backgrounds and obstacles, which significantly outperforms existing
state-of-the-art algorithms.
