论文标题
自动水下车辆的3D路径遵循和避免碰撞的深度加固学习控制器
Deep Reinforcement Learning Controller for 3D Path-following and Collision Avoidance by Autonomous Underwater Vehicles
论文作者
论文摘要
控制理论为工程师提供了多种设计控制器的工具，以操纵动力学系统的闭环行为和稳定性。这些方法在很大程度上依赖于有关管理物理系统的数学模型的见解。但是，在复杂的系统中，例如执行路径跟踪和避免碰撞的双重目标的自动水下车辆，决策变得无处不在。我们提出了一种使用最先进的深入增强学习（DRL）技术的解决方案，以开发能够实现此混合目标的自主剂，而无需对目标或环境具有先验知识。我们的结果表明，DRL在路径跟踪中的生存能力，并避免在极端障碍配置内自动驾驶汽车系统中实现人级决策。
Control theory provides engineers with a multitude of tools to design controllers that manipulate the closed-loop behavior and stability of dynamical systems. These methods rely heavily on insights about the mathematical model governing the physical system. However, in complex systems, such as autonomous underwater vehicles performing the dual objective of path-following and collision avoidance, decision making becomes non-trivial. We propose a solution using state-of-the-art Deep Reinforcement Learning (DRL) techniques, to develop autonomous agents capable of achieving this hybrid objective without having à priori knowledge about the goal or the environment. Our results demonstrate the viability of DRL in path-following and avoiding collisions toward achieving human-level decision making in autonomous vehicle systems within extreme obstacle configurations.
