CAREER: Topological Planning in Information Space for Intelligent Robotic Systems

职业：智能机器人系统信息空间拓扑规划

• 批准号: 1845227
• 负责人: Geoffrey Hollinger
• 金额: $ 49.75万
• 依托单位: Oregon State University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-03-15 至 2025-02-28
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1845227&HistoricalAwards=false
• 关键词: CAREER; Topological; Planning; Information; Space

This project envisions mobile robotic systems capable of retrieving streaming information about any physical location in the world for the benefit of scientists, first responders, and the general public. In the past decade, a number of marine and aerial systems have emerged in applications such as environmental monitoring, search and rescue, surveillance, and scientific exploration. However, currently an operator either teleoperates these vehicles directly or pre-specifies waypoint locations in a fixed plan. These approaches lead to severely limited performance by restricting the capabilities of robotic vehicles to (1) continue operating effectively as their environment, mission, and teammates change and (2) incorporate high-level goals given by human operators. This project seeks to bridge the gap between current systems that naively follow waypoints and intelligent systems capable of adapting to their environment, their team's capabilities, and to the goals of the operator. The ultimate objective of this project is to develop topological planning techniques based on homotopy and persistent homology that reason over a compact semantic representation of environmental conditions (e.g., informative areas, disturbances, and risky areas). By reasoning over concise semantic maps, the proposed methods will enable teams of autonomous vehicles to make decisions regarding information collection, cost minimization, and goal achievement in the physical world. The developed algorithms will be made publicly available through open source distribution and transitioned to field tests through ongoing collaborations with ocean scientists. The educational component of this project will integrate robotic vehicle planning into the graduate curriculum at Oregon State University through a Marine Robotics Certificate program and into Oregon high schools through an underwater robot programming module designed by the Oregon State University undergraduate Robotics Club.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.

该项目设想移动的机器人系统能够检索有关世界上任何物理位置的流信息，以造福科学家、急救人员和公众。在过去十年中，出现了许多海洋和航空系统，用于环境监测、搜索和救援、监视和科学探索等应用。然而，目前操作员要么直接遥控这些车辆，要么在固定的计划中预先指定航路点位置。这些方法通过限制机器人车辆的能力而导致严重受限的性能：（1）随着其环境、使命和队友的改变而继续有效地操作，以及（2）结合人类操作员给出的高水平目标。该项目旨在弥合当前系统之间的差距，天真地遵循航路点和智能系统能够适应他们的环境，他们的团队的能力，并以运营商的目标。该项目的最终目标是开发基于同伦和持久同源性的拓扑规划技术，该技术基于环境条件的紧凑语义表示（例如，信息区、干扰区和危险区）。通过对简洁的语义地图进行推理，所提出的方法将使自动驾驶汽车团队能够在物理世界中做出有关信息收集、成本最小化和目标实现的决策。开发的算法将通过开源分发公开提供，并通过与海洋科学家的持续合作过渡到现场测试。该项目的教育部分将通过海洋机器人证书课程将机器人车辆规划纳入俄勒冈州州立大学的研究生课程，并通过由俄勒冈州州立大学本科生机器人俱乐部设计的水下机器人编程模块将机器人车辆规划纳入俄勒冈州高中。该奖项反映了NSF的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准。

项目成果

Topology-Aware Self-Organizing Maps for Robotic Information Gathering

用于机器人信息收集的拓扑感知自组织地图

• DOI: 10.1109/iros45743.2020.9341040
• 发表时间: 2020
• 期刊: IEEE International Conference on Intelligent Robots and Systems
• 作者: McCammon, Seth;Jones, Dylan;Hollinger, Geoffrey A.
• 通讯作者: Hollinger, Geoffrey A.

Ocean front detection and tracking using a team of heterogeneous marine vehicles

• DOI: 10.1002/rob.22014
• 发表时间: 2021-01
• 期刊: Journal of Field Robotics
• 影响因子: 8.3
• 作者: Seth McCammon;Gilberto Marcon dos Santos;M. Frantz;T. Welch;Graeme Best;R. Shearman;J. Nash;J. Barth;J. Adams;Geoffrey A. Hollinger

Topological path planning for autonomous information gathering

• DOI: 10.1007/s10514-021-10012-x
• 发表时间: 2021-09-07
• 期刊: AUTONOMOUS ROBOTS
• 影响因子: 3.5
• 作者: McCammon, Seth;Hollinger, Geoffrey A.
• 通讯作者: Hollinger, Geoffrey A.

Online Exploration of Tunnel Networks Leveraging Topological CNN-based World Predictions

• DOI: 10.1109/iros45743.2020.9341170
• 发表时间: 2020-10
• 期刊: 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
• 作者: Manish Saroya;Graeme Best;Geoffrey A. Hollinger

Roadmap Learning for Probabilistic Occupancy Maps With Topology-Informed Growing Neural Gas

具有拓扑信息的生长神经气体的概率占用图的路线图学习

• DOI: 10.1109/lra.2021.3068886
• 发表时间: 2021
• 期刊: IEEE Robotics and Automation Letters
• 影响因子: 5.2
• 作者: Saroya, Manish;Best, Graeme;Hollinger, Geoffrey A.
• 通讯作者: Hollinger, Geoffrey A.