语义感知的预测性巡检路径规划
Semantics-aware Predictive Inspection Path Planning
AI总结：
本文提出SPP语义感知预测规划，通过识别语义场景图中的重复空间模式预测未见环境，并设计两种压载水舱巡检路径策略，仿真与真实船舶实验证明其可缩短巡检时间并保持覆盖率。
摘要：
本文提出了一种新颖的语义感知巡检路径规划范式，称为“语义感知预测规划”（Semantics-aware Predictive Planning，SPP）。需要对特定对象或结构（称为“语义”）进行巡检的工业环境，例如船舶内部的压载水舱，通常会呈现出感兴趣语义的结构化且重复的空间布置。受此启发，我们首先提出一种算法，用于在语义场景图表示中识别语义在空间上重复的模式（精确或非精确），并利用这些模式预测环境中未见部分内图的演化。此外，本文还提出了两种专为压载水舱巡检定制、并利用这些预测的巡检路径规划策略。为评估这一新型预测规划范式的性能，研究开展了仿真和实验两方面评估。首先，我们进行了一项仿真研究，将该方法与相关的最先进技术进行比较，并进一步给出测试，展示其处理不完美模式的能力。其次，我们将该方法部署到一架可容忍碰撞的空中机器人上，该机器人在两艘真实船舶的压载舱内运行。仿真和现场实验结果均表明，在保持相同或更好语义表面覆盖率的同时，巡检时间相较最先进方法有显著改善。一组描述该方法不同部分及现场部署的视频可在 https://tinyurl.com/spp-videos 获取。本工作的代码发布于 https://github.com/ntnu-arl/predictive_planning_ros。
英文摘要：
This paper presents a novel semantics-aware inspection path planning paradigm called "Semantics-aware Predictive Planning" (SPP). Industrial environments that require the inspection of specific objects or structures (called "semantics"), such as ballast water tanks inside ships, often present structured and repetitive spatial arrangements of the semantics of interest. Motivated by this, we first contribute an algorithm that identifies spatially repeating patterns of semantics - exact or inexact - in a semantic scene graph representation and makes predictions about the evolution of the graph in the unseen parts of the environment using these patterns. Furthermore, two inspection path planning strategies, tailored to ballast water tank inspection, that exploit these predictions are proposed. To assess the performance of the novel predictive planning paradigm, both simulation and experimental evaluations are performed. First, we conduct a simulation study comparing the method against relevant state-of-the-art techniques and further present tests showing its ability to handle imperfect patterns. Second, we deploy our method onboard a collision-tolerant aerial robot operating inside the ballast tanks of two real ships. The results, both in simulation and field experiments, demonstrate significant improvement over the state-of-the-art in terms of inspection time while maintaining equal or better semantic surface coverage. A set of videos describing the different parts of the method and the field deployments is available at https://tinyurl.com/spp-videos. The code for this work is made available at https://github.com/ntnu-arl/predictive_planning_ros.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
