用多艘无人潜航器高效定位等深线，兼顾不确定性与实际水下挑战。
Efficient Feature Mapping Using a Collaborative Team of AUVs
- 设计新目标函数，基于不确定性评估提升等值面估计精度。
- 实验证明在通信慢、算力弱条件下仍保持理论性能保障。
- 适合关注水下自主导航与团队协同的科研与工程人员。
我们通过小型自主水下航行器（AUV）团队实验，确定等深线位置。主要贡献包括：(1) 提出一种新型水平集估计目标函数，利用严格的不确定性评估；(2) 描述了在真实环境中部署该方法所需应对的实际挑战及解决方案。结合路径规划与已有去中心化方法，实现理论性能保证。实验表明，即使在水下机器人常见限制——如通信缓慢且间歇性、计算资源有限——下，该方法仍可保持预期性能表现。
原文摘要 · Abstract (English)
We present the results of experiments performed using a team of small autonomous underwater vehicles (AUVs) to determine the location of an isobath. The primary contributions of this work are (1) the development of a novel objective function for level set estimation that utilizes a rigorous assessment of uncertainty, and (2) a description of the practical challenges and corresponding solutions needed to implement our approach in the field using a team of AUVs. We combine path planning techniques and an approach to decentralization from prior work that yields theoretical performance guarantees. Experimentation with a team of AUVs provides empirical evidence that the desirable performance guarantees can be preserved in practice even in the presence of limitations that commonly arise in underwater robotics, including slow and intermittent acoustic communications and limited computational resources.
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。