Many existing obstacle avoidance algorithms overlook the crucial balance between safety and agility, especially in environments of varying complexity. In our study, we introduce an obstacle avoidance pipeline based on reinforcement learning. This pipeline enables drones to adapt their flying speed according to the environmental complexity. Moreover, to improve the obstacle avoidance performance in cluttered environments, we propose a novel latent space. The latent space in this representation is explicitly trained to retain memory of previous depth map observations. Our findings confirm that varying speed leads to a superior balance of success rate and agility in cluttered environments. Additionally, our memory-augmented latent representation outperforms the latent representation commonly used in reinforcement learning. Finally, after minimal fine-tuning, we successfully deployed our network on a real drone for enhanced obstacle avoidance.

翻译：现有许多避障算法忽视了安全性与敏捷性的关键平衡，尤其是在复杂度各异的环境中。本研究提出一种基于强化学习的避障流程，使无人机能够根据环境复杂度动态调整飞行速度。此外，为提升在杂乱环境中的避障性能，我们设计了一种新的隐空间：该表示中的隐空间经过显式训练，可保留对先前深度图观测的记忆。实验结果证实，变速飞行能在杂乱环境中实现成功率和敏捷性的更优平衡。同时，我们的记忆增强隐空间优于强化学习中常用的隐空间。最终，经少量微调后，我们成功将该网络部署于真实无人机，实现了增强型避障功能。