CRII: RI: Vision-Anchored Automation of Bird-Sized UAVs in Unknown Cluttered Indoor Environments

CRII：RI：未知杂乱室内环境中鸟类大小无人机的视觉锚定自动化

• 批准号: 2242243
• 负责人: Dongfang Liu
• 金额: $ 17.47万
• 依托单位: Rochester Institute of Tech
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-02-01 至 2025-01-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2242243&HistoricalAwards=false
• 关键词: CRII; RI; Vision; Anchored; Automation

In many real-world applications, autonomous unmanned aerial vehicles (UAVs) can be used to explore unknown, cluttered indoor spaces where GPS access and communication are often denied. To accommodate the confined working space, however, UAVs have a small body size (roughly the size of a bird). Such small size UVAs require lightweight and power-efficient sensors. Therefore, this research project aims to develop full automation for bird-sized UAVs within unknown and cluttered indoor environments using only an RGB-D camera. Even though vision-only UAVs have advantages for system assembly, their automation becomes increasingly difficult when other sensors, such as radars and LiDARs, are not available. This project addresses two fundamental challenges for UAV automation: (1) how to construct visual perception to have a holistic yet computationally efficient understanding of the surrounding environment using only a vision sensor and (2) leveraging the established perception system, how to employ visual navigation to perform target-driven, safety-critical operations without relying on maps or GPS. The research of this project is closely integrated with education and outreach activities at the Rochester Institute of Technology.This research develops technologies for vision-anchored automation of bird-sized UAVs in unknown cluttered indoor environments. The project introduces a method for multi-task fusion to simultaneously detect, segment, and track regions of interest within the video frames. The proposed perception system can achieve a holistic, sensor-fusion-like understanding of the scene using only measurements from the RGB-D camera sensor. The computational tractability of the perception model will be investigated to ensure sufficient operation endurance by leveraging a novel paradigm of recursive knowledge distillation. For navigation tasks, the project includes a novel policy-learning scheme—empowered by multimodal representations conditioned on observations—in conjunction with an online domain adaptation technique that could enable bird-sized UAVs to make appropriate decisions in various critical operations, e.g., target searching and collision avoidance during navigation in a cluttered and confined working space.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.

在许多实际应用中，自主无人驾驶飞行器（uav）可用于探索未知的、混乱的室内空间，在这些空间中，GPS访问和通信通常被拒绝。然而，为了适应有限的工作空间，无人机有一个小的机身尺寸（大约一只鸟的大小）。这种小尺寸的无人机需要重量轻且节能的传感器。因此，该研究项目旨在仅使用RGB-D相机，在未知和混乱的室内环境中开发完全自动化的鸟大小无人机。尽管纯视觉无人机在系统组装方面具有优势，但当其他传感器（如雷达和激光雷达）不可用时，它们的自动化变得越来越困难。该项目解决了无人机自动化的两个基本挑战：(1)如何构建视觉感知，以便仅使用视觉传感器对周围环境进行全面而高效的计算理解；(2)利用已建立的感知系统，如何利用视觉导航在不依赖地图或GPS的情况下执行目标驱动的安全关键操作。该项目的研究与罗切斯特理工学院的教育和外展活动紧密结合。本研究开发了鸟大小无人机在未知杂乱室内环境下的视觉锚定自动化技术。该项目介绍了一种多任务融合的方法，可以同时检测、分割和跟踪视频帧内感兴趣的区域。所提出的感知系统仅使用RGB-D相机传感器的测量数据就可以实现对场景的整体、传感器融合式理解。通过利用递归知识蒸馏的新范式，研究感知模型的计算可追溯性，以确保足够的运行耐力。对于导航任务，该项目包括一种新的策略学习方案，该方案由以观测为条件的多模态表示支持，并结合在线域适应技术，使鸟大小的无人机能够在各种关键操作中做出适当的决策，例如，在混乱和受限的工作空间中导航过程中的目标搜索和避撞。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。