发表机构
KTH Royal Institute of Technology; University of Michigan（瑞典皇家理工学院; 密歇根大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究通过模拟实验，利用HYCOM数据和射线追踪，量化了折射引起的测距偏差对千米级水下多智能体协同定位轨迹的显著影响，尤其在声速梯度陡峭区域。
AI 中文摘要
本研究探讨了声学测距中的折射偏差如何在多种海洋学条件和空间尺度下影响多智能体协同定位。虽然多智能体测距辅助导航（利用对固定基础设施或其他智能体的测距测量）是大规模水下定位挑战的一种有前景的解决方案，但其精度在很大程度上取决于测距测量的质量。声速变化会引起声线的折射（弯曲），然而，为了算法的可处理性，标准传感器融合流程假设直线传播。这种折射系统性地使测距测量比直线假设预测的要长。然而，这种偏差对千米级多智能体协同定位的影响仍未得到探索。我们进行了一系列模拟实验，涉及多个在千米尺度上运行的智能体。模拟使用HYCOM再分析数据来重建真实的海洋学条件，使用射线追踪来生成考虑折射的测距，并使用集中式多智能体因子图估计器来量化由此产生的对估计轨迹的测量偏差。初步结果表明，折射引起的偏差可导致估计轨迹显著退化，尤其是在声速梯度陡峭的区域。我们还分享了模拟环境以支持进一步研究，网址为 https://this https URL。
英文摘要
This work studies how refraction-induced bias on acoustic ranging affects multi-agent collaborative localization in a range of oceanographic conditions and spatial scales. While multi-agent range-aided navigation, which uses range measurements to either fixed infrastructure or other agents, is a promising solution to the challenges of large-scale underwater localization, its accuracy depends strongly on the quality of range measurements. Sound speed variability induces refraction (bending) of acoustic rays, yet, for algorithmic tractability, standard sensor fusion pipelines assume straight-line propagation. This refraction systematically biases range measurements to be longer than the straight-line assumption predicts. However, the effects of this bias on multi-agent collaborative localization on kilometer scales remains unexplored. We present a series of simulated experiments with several agents operating over kilometer scales. The simulation uses HYCOM reanalysis data to recreate realistic oceanographic conditions, ray tracing to generate refraction-informed ranges, and a centralized multi-agent factor graph estimator to quantify the resulting measurement bias on estimated trajectories. Preliminary results indicate that refraction-induced bias can induce significant degradation of estimated trajectories, particularly in regions with sharp sound-speed gradients. We also share the simulation environment to support further studies https://github.com/UMich-RobotExploration/manta-ray.
Comments6 pages, 8 figures, to be published in OCEANS
