水下机器人机载视觉与MPC导航：面向多机器人实验与对接的开源BlueROV2平台
Onboard Vision and MPC Navigation for Underwater Robots: An Open BlueROV2 Platform for Multi-Robot Experiments & Docking
- KTH Royal Institute of Technology（瑞典皇家理工学院）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本文提出一个集成机载视觉与非线性模型预测控制的开源BlueROV2平台，实现水下自主导航与对接，并提供仿真工具与物理对接站，实验验证了感知、估计、跟踪与对接性能。
AI中文摘要：
自主水下机器人需要鲁棒的感知、估计与控制才能在受限环境中运行。本文提出了一个开源BlueROV2平台，将机载视觉与非线性模型预测控制（NMPC）相结合，用于自主导航与对接。该平台在模块化耐压壳体中集成了NVIDIA Jetson Orin NX和Intel RealSense D435i立体相机。水下标定的立体深度与实时目标检测提供了附近BlueROV2机器人在相机坐标系和机体坐标系中的相对位置测量。基于四元数的估计器融合外部位姿与惯性测量，而基于非线性六自由度模型的NMPC控制器则跟踪规划的导航与对接轨迹。为支持可复现开发，我们还提供了基于物理的开源PX4 SITL与Gazebo环境、多机器人仿真工具以及低成本物理对接站。实验评估了水下感知、机载计算性能、状态估计、轨迹跟踪与自主对接。
英文摘要：
Autonomous underwater robots require robust perception, estimation and control to operate in confined environments. This paper presents an open-source BlueROV2 platform combining onboard vision with nonlinear Model Predictive Control (NMPC) for autonomous navigation and docking. The platform integrates an NVIDIA Jetson Orin NX and an Intel RealSense D435i stereo camera in a modular pressure housing. Underwater-calibrated stereo depth and realtime object detection provide relative position measurements of nearby BlueROV2 vehicles in the camera and body frames. A quaternion-based estimator fuses external pose and inertial measurements, while an NMPC controller based on a nonlinear six-degree-of-freedom model tracks planned navigation and docking trajectories. To support reproducible development, we also provide open-source physics-based PX4 SITL and Gazebo environments, multi-robot simulation tools and a lowcost physical docking station. Experiments evaluate underwater perception, onboard computational performance, state estimation, trajectory tracking and autonomous docking.
