NRI: EAGER: Teaching Aerial Robots to Perch Like a Bat via AI-Guided Design and Control

NRI：EAGER：通过人工智能引导设计和控制教导空中机器人像蝙蝠一样栖息

• 批准号: 1944964
• 负责人: Sarah Ostadabbas
• 金额: $ 10.24万
• 依托单位: Northeastern University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-10-01 至 2021-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1944964&HistoricalAwards=false
• 关键词: NRI; EAGER; Teaching; Aerial; Robots

This research will develop the scientific and technological foundations for design of small unmanned aerial vehicles (UAVs) with bat shapes and flying abilities. These bat-like UAVs will be non-intrusive and safe to operate in shared spaces to provide situational awareness to humans. Our bat-inspired design will also be collision-tolerant to negotiate cluttered, hard-to-access environments in the physical world. The resulting technology can significantly improve public safety and vehicular dynamic traffic control in smart cities and cost-effectiveness associated with monitoring environmental disasters. Ultimately, the UAV can provide computing, communication and sensing capabilities in large-scale systems such as, residential buildings, streets, construction zones, and state parks. These capabilities should result in enormous societal impact and economic benefit. In addition, as the result of this project, a new generation of scientists and engineers will be trained in addressing multidisciplinary challenges at the intersection of theory and experiment. The project will create programs and tools to train workforce with new skills including bio-inspired robotics, machine learning and artificial intelligence, and nonlinear control theory. This research adopts an artificial intelligence-guided framework to study bat's flight maneuvers including perching (i.e. upside-down landing), zero-path flight, and hovering. Due to the fact that the salient aspects of the bat's wing motion can be represented in a low-dimensional subspace, we will apply an auto-encoding variational inference approach on the flying data from real animal in order to extract low dimensional, yet interpretable, embedding of the its underlying flight model. We will also use a high-fidelity virtual environment for 3D modeling, synthetic design and validation of the extracted low-dimensional latent variables. This will also lead to better understating of the bat sensory feedback mechanism through a data-driven procedure. Our research objective will simplify the engineering procedure to design bio-inspired aerial co-robots that closely mimic the flight behavior of a target animal, therefore is directly towards lowering the barriers for understanding fundamentals regarding closed-loop control and design of bio-inspired multimodal co-robots. In order to achieve the proposed research objectives, we will center our effort around conducting two main phases during the one year timeline of this project: first, AI-guided analysis and modeling of bat's various flight maneuvers, and second, development of a soft and collision-tolerant bat-inspired aerial agent capable of landing on structures. These two phases will be accomplished by team's cross-disciplinary collaborative, as components are highly interlinked and dependent.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.

这项研究将开发出具有蝙蝠形状和飞行能力的小型无人机（UAV）设计的科学和技术基础。这些类似蝙蝠的无人机将在共享空间中进行操作，以提供对人类的情境意识。我们以蝙蝠为风格的设计也将具有耐受性，以协商物理世界中的混乱，难以及的环境。最终的技术可以显着改善智能城市的公共安全和车辆动态交通管制以及与监测环境灾难相关的成本效益。最终，无人机可以在大规模系统中提供计算，通信和传感功能，例如住宅建筑，街道，建筑区和州立公园。这些能力应导致巨大的社会影响和经济利益。此外，作为该项目的结果，新一代的科学家和工程师将接受培训，以应对理论与实验的交集解决多学科挑战。该项目将创建程序和工具，以培训新技能，包括生物启发的机器人技术，机器学习和人工智能以及非线性控制理论。这项研究采用了人工智能指导的框架来研究蝙蝠的飞行操作，包括栖息（即倒置着陆），零路径飞行和盘旋。由于可以在低维子空间中表示蝙蝠机翼运动的显着方面，因此我们将对来自真实动物的飞行数据应用自动编码的变异推理方法，以提取其基础飞行模型的低维，可解释但易于解释的嵌入。我们还将使用高保真虚拟环境进行3D建模，合成设计和提取的低维潜在变量的验证。这也将通过数据驱动程序更好地低估对BAT的感觉反馈机制。我们的研究目标将简化工程程序，以设计受生物启发的空中共同驾驶，以密切模仿目标动物的飞行行为，因此直接旨在降低障碍，以了解有关生物启发的多模态共同机器人的闭环控制和设计的基础知识。为了实现拟议的研究目标，我们将围绕在该项目的一年时间表中进行两个主要阶段进行努力：首先，AI引导的分析和对BAT的各种飞行操作的分析和建模，其次，开发了一种柔和的耐受性蝙蝠启发的空中气氛，能够在结构上着陆。这两个阶段将由团队的跨学科合作完成，因为组件高度相互链接和依赖。该奖项反映了NSF的法定任务，并认为使用基金会的知识分子和更广泛影响的评估标准，认为值得通过评估来获得支持。

项目成果

Deep Switching Auto-Regressive Factorization: Application to Time Series Forecasting

• DOI: 10.1609/aaai.v35i8.16907
• 发表时间: 2020-09
• 作者: Amirreza Farnoosh;Bahar Azari;S. Ostadabbas

Computational Structure Design of a Bio-Inspired Armwing Mechanism

• DOI: 10.1109/lra.2020.3010217
• 发表时间: 2020-07
• 期刊: IEEE Robotics and Automation Letters
• 影响因子: 5.2
• 作者: Eric N. Sihite;Peter Kelly;A. Ramezani