UIVNAV: Underwater Information-driven Vision-based Navigation via Imitation Learning
arxiv(2023)
摘要
Autonomous navigation in the underwater environment is challenging due to
limited visibility, dynamic changes, and the lack of a cost-efficient accurate
localization system. We introduce UIVNav, a novel end-to-end underwater
navigation solution designed to drive robots over Objects of Interest (OOI)
while avoiding obstacles, without relying on localization. UIVNav uses
imitation learning and is inspired by the navigation strategies used by human
divers who do not rely on localization. UIVNav consists of the following
phases: (1) generating an intermediate representation (IR), and (2) training
the navigation policy based on human-labeled IR. By training the navigation
policy on IR instead of raw data, the second phase is domain-invariant – the
navigation policy does not need to be retrained if the domain or the OOI
changes. We show this by deploying the same navigation policy for surveying two
different OOIs, oyster and rock reefs, in two different domains, simulation,
and a real pool. We compared our method with complete coverage and random walk
methods which showed that our method is more efficient in gathering information
for OOIs while also avoiding obstacles. The results show that UIVNav chooses to
visit the areas with larger area sizes of oysters or rocks with no prior
information about the environment or localization. Moreover, a robot using
UIVNav compared to complete coverage method surveys on average 36
when traveling the same distances. We also demonstrate the feasibility of
real-time deployment of UIVNavin pool experiments with BlueROV underwater robot
for surveying a bed of oyster shells.
更多查看译文
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要