提出实时闭环导航框架，让无人潜航器在未知水域安全高效避障。
Instantaneous Planning, Control and Safety for Navigation in Unknown Underwater Spaces
- 基于实时传感器数据设计反馈控制器，动态生成轨迹
- 仿真中定位误差显著低于传统PID方法，进入通信范围更稳定
- 适合复杂水下环境下的自主导航，尤其适用于信号弱区域
在未知水域中导航自主水下航行器（AUV）面临能见度差、信号传输弱和水流扰动等挑战，导致全局定位不准、通信不可靠及避障困难。局部感知提供实时环境信息以支持在线决策，但水下传感器测量固有的噪声引入不确定性，加剧了规划与控制的难度。为此，我们提出一种集成规划与控制的框架，利用实时传感数据动态生成闭环轨迹，确保在狭窄空间中具备鲁棒避障能力和增强机动性。通过基于预先设计的反馈控制器进行运动规划，该方法降低了在线优化所需的计算复杂度，提升了复杂水下环境中的操作安全性。所提方法在ROS Gazebo环境中对RexRov AUV进行了仿真验证，性能通过与基于PID的跟踪方法对比评估，并量化了航行器进入目标通信范围时死记漂移的定位误差。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。