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.

这项研究将为设计具有蝙蝠形状和飞行能力的小型无人机（UAV）奠定科学和技术基础。这些类似蝙蝠的无人机将是非侵入性的，可以安全地在共享空间中运行，为人类提供态势感知。我们的蝙蝠灵感的设计也将是碰撞容忍谈判混乱，难以访问的环境中的物理世界。由此产生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控制，以及与监测环境灾害相关的成本效益。最终，无人机可以在大型系统中提供计算，通信和传感能力，例如住宅楼，街道，建筑区和州立公园。这些能力将产生巨大的社会影响和经济效益。此外，作为该项目的结果，新一代科学家和工程师将接受培训，以应对理论和实验交叉点的多学科挑战。该项目将创建程序和工具，以培训具有新技能的劳动力，包括生物启发机器人，机器学习和人工智能以及非线性控制理论。本研究采用人工智慧导引架构，研究蝙蝠的飞行动作，包括栖息、零路径飞行及盘旋。由于蝙蝠的翅膀运动的突出方面可以表示在一个低维子空间的事实，我们将应用一个自动编码变分推理方法从真实的动物的飞行数据，以提取低维，但可解释的，嵌入其底层的飞行模型。我们还将使用高保真虚拟环境进行3D建模，合成设计和验证提取的低维潜变量。这也将导致通过数据驱动的程序更好地理解蝙蝠的感官反馈机制。我们的研究目标将简化工程程序，以设计生物启发的空中合作机器人，密切模仿目标动物的飞行行为，因此直接降低了理解闭环控制和生物启发的多模态合作机器人设计的基础知识的障碍。为了实现拟议的研究目标，我们将在该项目的一年时间轴内集中精力进行两个主要阶段：第一，人工智能引导的蝙蝠各种飞行动作的分析和建模，第二，开发一种软的和耐碰撞的蝙蝠启发的空中代理，能够降落在结构上。这两个阶段将通过团队的跨学科合作来完成，因为各个组成部分是高度相互关联和相互依赖的。该奖项反映了NSF的法定使命，并被认为值得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来支持。

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