NSF-BSF: RI: Small: Provably High-Quality Robot Inspection Planning - Theory and Application

NSF-BSF：RI：小型：可证明的高质量机器人检测规划 - 理论与应用

• 批准号: 2008475
• 负责人: Ron Alterovitz
• 金额: $ 44.85万
• 依托单位: University of North Carolina at Chapel Hill
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-10-01 至 2024-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2008475&HistoricalAwards=false
• 关键词: NSF; BSF; RI; Small; Provably

Inspecting the surfaces of objects is a common task required in a variety of applications, from diagnosing a diseased organ by inspecting its tissue surface to verifying the safety of a bridge by inspecting its structure. Robots with mounted sensors have the potential to efficiently and effectively perform such inspection tasks by automatically moving the sensor to view the region of interest on the object's surface while avoiding obstacles and satisfying motion constraints. For example, an endoscopic needle-based robot has the potential to perform an inspection task inside the human body to help diagnose certain diseases that manifest on the surfaces of internal organs. As another example, drones have the potential to be widely used to efficiently inspect the complex geometry of bridges, which is increasingly important since almost 40% of the nation's bridges exceed their 50-year design life, and regular inspections are critical to ensuring bridge safety. In this project, the research team will investigate the problem of robot inspection planning. Specifically, the team will develop and analyze new, efficient computational methods to plan motions for a robot to enable the robot to autonomously maximize the quality of an inspection while safely avoiding obstacles.The goal of this project is to provide a theoretically-grounded efficient and effective algorithmic framework for robust inspection planning demonstrated on real-world robotics applications. Specifically, the research team plans to provide a rigorous explanation to why and when different variants of the inspection-planning problem are computationally hard. Pinpointing exactly why the inspection problem is computationally hard will then enable the research team to develop an efficient algorithmic framework to solve the fundamental version of the inspection-planning problem, namely, when there is no uncertainty with respect to the robot's kinematic model, sensor model, or environment model. Finally, the research team plans to extend the new algorithmic framework to account for different sources of uncertainty in order to make the results applicable to real-world problems. This will be done by combining tools from diverse domains such as computational geometry, graph theory, optimization, and machine learning. In all stages of the project, the research team will demonstrate the results using several applications in a laboratory setting, including the inspection of patient anatomy using needle-based robots and the inspection of bridges using drones.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

在各种应用中，从检查组织表面来诊断疾病器官，到通过检查结构来验证桥梁的安全性，检查对象的表面是一项常见的任务。安装了传感器的机器人有可能通过自动移动传感器来查看对象表面的感兴趣区域，从而高效地执行此类检查任务，同时避开障碍物并满足运动约束。例如，基于内窥镜针头的机器人有可能在人体内执行检查任务，以帮助诊断某些出现在内部器官表面的疾病。另一个例子是，无人机有可能被广泛用于有效地检查复杂的桥梁几何形状，这一点越来越重要，因为美国近40%的桥梁超过了50年的设计寿命，定期检查对于确保桥梁安全至关重要。在这个项目中，研究团队将对机器人检查规划问题进行研究。具体地说，该团队将开发和分析新的、高效的计算方法来规划机器人的运动，使机器人能够在安全避开障碍物的同时自主地最大化检查质量。该项目的目标是提供一个理论上有基础的高效和有效的算法框架，用于在现实世界机器人应用中演示稳健的检查规划。具体地说，研究小组计划对检查规划问题的不同变体为什么以及何时计算困难提供严格的解释。准确地确定为什么检查问题在计算上是困难的，这将使研究团队能够开发出有效的算法框架来解决检查规划问题的基本版本，即当机器人的运动学模型、传感器模型或环境模型不存在不确定性时。最后，研究小组计划扩展新的算法框架，以解决不同的不确定性来源，以便使结果适用于现实世界的问题。这将通过结合计算几何、图论、优化和机器学习等不同领域的工具来完成。在该项目的所有阶段，研究团队将在实验室环境中使用几个应用程序来展示结果，包括使用针基机器人检查患者解剖结构和使用无人机检查桥梁。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

A Metric for Finding Robust Start Positions for Medical Steerable Needle Automation

寻找医疗可操纵针自动化稳健起始位置的指标

• DOI: 10.1109/iros47612.2022.9982227
• 发表时间: 2022
• 期刊: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS
• 作者: Hoelscher, Janine;Fried, Inbar;Fu, Mengyu;Patwardhan, Mihir;Christman, Max;Akulian, Jason;Webster, Robert J.;Alterovitz, Ron
• 通讯作者: Alterovitz, Ron

Computationally-Efficient Roadmap-based Inspection Planning via Incremental Lazy Search

通过增量惰性搜索进行基于计算效率的路线图的检查规划

• 发表时间: 2021
• 期刊: IEEE International Conference on Robotics and Automation
• 作者: Fu, Mengyu;Salzman, Oren;Alterovitz, Ron
• 通讯作者: Alterovitz, Ron

Asymptotically optimal inspection planning via efficient near-optimal search on sampled roadmaps

通过对采样路线图进行有效的近最优搜索来实现渐近最优检查计划

• DOI: 10.1177/02783649231171646
• 发表时间: 2023
• 期刊: The International Journal of Robotics Research
• 作者: Fu, Mengyu;Kuntz, Alan;Salzman, Oren;Alterovitz, Ron
• 通讯作者: Alterovitz, Ron

Autonomous medical needle steering in vivo

• DOI: 10.1126/scirobotics.adf7614
• 发表时间: 2023-09-20
• 期刊: SCIENCE ROBOTICS
• 影响因子: 25
• 作者: Kuntz，Alan;Emerson，Maxwell;Alterovitz，Ron
• 通讯作者: Alterovitz，Ron