论文标题
通过深度加强学习，平稳轨迹碰撞避免
Smooth Trajectory Collision Avoidance through Deep Reinforcement Learning
论文作者
论文摘要
避免碰撞是视觉指导自主导航的至关重要任务。基于深入增强学习（DRL）的解决方案变得越来越流行。在这项工作中，我们提出了几种新颖的代理状态和奖励功能设计，以解决基于DRL的导航解决方案中的两个关键问题：1）训练有素的飞行轨迹的平稳性； 2）模型概括以处理看不见的环境。 我们的模型在DRL框架下配制，依赖于保证金奖励和平滑度约束，以确保无人机飞行平稳，同时大大减少了碰撞的机会。提出的平滑度奖励最大程度地减少了飞行轨迹的一阶和二阶导数的组合，这也可以驱动要均匀分布的点，从而导致飞行速度稳定。为了增强代理商处理新的看不见环境的能力，提出了两个实用的设置，以提高状态和奖励功能在不同场景中的部署时的不变性。实验证明了我们的整体设计和各个组件的有效性。
Collision avoidance is a crucial task in vision-guided autonomous navigation. Solutions based on deep reinforcement learning (DRL) has become increasingly popular. In this work, we proposed several novel agent state and reward function designs to tackle two critical issues in DRL-based navigation solutions: 1) smoothness of the trained flight trajectories; and 2) model generalization to handle unseen environments. Formulated under a DRL framework, our model relies on margin reward and smoothness constraints to ensure UAVs fly smoothly while greatly reducing the chance of collision. The proposed smoothness reward minimizes a combination of first-order and second-order derivatives of flight trajectories, which can also drive the points to be evenly distributed, leading to stable flight speed. To enhance the agent's capability of handling new unseen environments, two practical setups are proposed to improve the invariance of both the state and reward function when deploying in different scenes. Experiments demonstrate the effectiveness of our overall design and individual components.
