NavRL：在动态环境中学习安全飞行
NavRL: Learning Safe Flight in Dynamic Environments
AI总结：
本文提出基于PPO的NavRL框架，通过精心设计的状态动作表示和安全防护，实现无人机在动态环境中零样本迁移的安全导航，且碰撞次数最少。
摘要：
在动态环境中安全飞行要求无人机（UAV）在穿越具有移动障碍物的杂乱空间时做出有效决策。传统方法通常将决策分解为用于预测和规划的分层模块。尽管这些手工设计的系统在特定场景中表现良好，但当环境条件变化时可能会失效，并且通常需要仔细的参数调整。此外，由于采用不准确的数学模型假设和旨在实现计算效率的简化，其解决方案可能不是最优的。为克服这些局限性，本文引入了NavRL框架，这是一种基于近端策略优化（PPO）算法的深度强化学习导航方法。NavRL利用我们精心设计的状态和动作表示，使学习到的策略能够在静态和动态障碍物并存的情况下做出安全决策，并实现从仿真到真实世界飞行的零样本迁移。此外，所提出的方法采用了一种简单但有效的安全防护机制，灵感来源于速度障碍概念，以缓解与神经网络黑盒性质相关的潜在失败。为加速收敛，我们使用NVIDIA Isaac Sim实现训练流程，支持数千架四旋翼的并行训练。仿真和物理实验表明，我们的方法能够在动态环境中确保安全导航，并且与基准相比碰撞次数最少。
英文摘要：
Safe flight in dynamic environments requires unmanned aerial vehicles (UAVs) to make effective decisions when navigating cluttered spaces with moving obstacles. Traditional approaches often decompose decision-making into hierarchical modules for prediction and planning. Although these handcrafted systems can perform well in specific settings, they might fail if environmental conditions change and often require careful parameter tuning. Additionally, their solutions could be suboptimal due to the use of inaccurate mathematical model assumptions and simplifications aimed at achieving computational efficiency. To overcome these limitations, this paper introduces the NavRL framework, a deep reinforcement learning-based navigation method built on the Proximal Policy Optimization (PPO) algorithm. NavRL utilizes our carefully designed state and action representations, allowing the learned policy to make safe decisions in the presence of both static and dynamic obstacles, with zero-shot transfer from simulation to real-world flight. Furthermore, the proposed method adopts a simple but effective safety shield for the trained policy, inspired by the concept of velocity obstacles, to mitigate potential failures associated with the black-box nature of neural networks. To accelerate the convergence, we implement the training pipeline using NVIDIA Isaac Sim, enabling parallel training with thousands of quadcopters. Simulation and physical experiments show that our method ensures safe navigation in dynamic environments and results in the fewest collisions compared to benchmarks.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
