Navigating autonomous underwater vehicles (AUVs) in unknown environments is significantly challenging due to poor visibility, weak signal transmission, and dynamic water currents. These factors pose challenges in accurate global localization, reliable communication, and obstacle avoidance. Local sensing provides critical real time environmental data to enable online decision making. However, the inherent noise in underwater sensor measurements introduces uncertainty, complicating planning and control. To address these challenges, we propose an integrated planning and control framework that leverages real time sensor data to dynamically induce closed loop AUV trajectories, ensuring robust obstacle avoidance and enhanced maneuverability in tight spaces. By planning motion based on pre designed feedback controllers, the approach reduces the computational complexity needed for carrying out online optimizations and enhances operational safety in complex underwater spaces. The proposed method is validated through ROS Gazebo simulations on the RexRov AUV, demonstrating its efficacy. Its performance is evaluated by comparison against PID based tracking methods, and quantifying localization errors in dead reckoning as the AUV transitions into the target communication range.

翻译：在未知环境中导航自主水下机器人（AUV）面临显著挑战，主要由于能见度低、信号传输弱以及动态水流等因素。这些条件对精确的全局定位、可靠通信和避障构成困难。局部传感可提供关键实时环境数据以支持在线决策，但水下传感器测量中的固有噪声会引入不确定性，使规划与控制复杂化。为解决这些问题，我们提出一种集成规划与控制框架，利用实时传感器数据动态生成闭环AUV轨迹，确保在狭窄空间内实现鲁棒避障并增强机动性。通过基于预设计反馈控制器规划运动，该方法降低了在线优化所需计算复杂度，并提升了复杂水下空间中的操作安全性。所提方法通过RexRov AUV上的ROS Gazebo仿真验证其有效性，并与基于PID的跟踪方法进行性能对比评估，同时量化AUV进入目标通信范围时航位推算中的定位误差。