S&AS: INT: RoboBees 2.0 Towards Autonomous Micro Air Vehicles

S

• 批准号: 1724197
• 负责人: Gu-Yeon Wei
• 金额: $ 40万
• 依托单位: Harvard University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-09-01 至 2021-02-28
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1724197&HistoricalAwards=false
• 关键词: S& INT; RoboBees; 2.0; Towards

In 2009, a group of researchers from Harvard led an NSF Expeditions in Computing project to build a colony of flapping-wing robots, called RoboBees, motivated by the multidisciplinary challenges associated with building and controlling effective robotic insects. The research has been exciting and it has tickled the imagination of many "young and old" through numerous museum exhibits and outreach activities. The severe inherent constraints associated with building at-scale flying robotic insects required many innovations and new technologies at each step. For example, a new manufacturing process called pop-up MEMS was developed to enable mass production of small-scale, foldable devices. New electronics were developed to flap artificial insect-scale wings. A new small-scale computer chip (called the BrainSoC), connected to various sensors, was created to control the robot. The culmination of this work has been exciting demonstrations of RoboBees hovering and maneuvering about within carefully controlled environments. The next phase of this work is to imbue these robots with machine intelligence and autonomy: RoboBee 2.0. The main objective of this proposal will be to teach the RoboBees to fly autonomously.Over the past 10 years, while roboticists have been busily building small-scale robots, there has been a surge of activity in machine learning that has led to rapid advances in machine perception and control. For example, the recent success of deep learning can be attributed to the virtuous cycle of (i) more and higher quality data; (ii) faster parallel computation; and (iii) more efficient learning algorithms. The time is ripe to combine these threads of research to develop machine learning-enabled flight control and perception for RoboBees. This project brings together a multidisciplinary team of experts from different engineering backgrounds to build the next generation of RoboBees. The project seeks to push the envelope by targeting the RoboBees platform, which introduces flight dynamics and sensitivity requirements beyond the bleeding edge of what is possible using off-the-shelf components. This effort builds on the existing experimental RoboBee platform at Harvard built with special onboard electronics, which will be used to record large volumes of flight data. This data can then feed exploration of machine learning flight control algorithms, which begins with simple hovering before tackling more challenging maneuvers such as obstacle avoidance and object tracking. Since hand tuning conventional control algorithms is overly cumbersome, focus will be on modern computing paradigms that can be taught rather than programmed. Development and demonstration of autonomous flight control based on deep learning for insect-scale flapping-wing robots will broadly impact the fields of microrobotics, machine learning, energy-efficient computing, and a broad array of autonomous systems, further extending capabilities of autonomy, to a broad range of robotic platforms, from regular vehicles to tiny robots of diverse configurations and applications.

2009年，来自哈佛的一组研究人员领导了一个NSF Expeditions in Computing项目，建立了一个名为RoboBees的扑翼机器人群体，其动机是与构建和控制有效的机器昆虫相关的多学科挑战。这项研究令人兴奋，通过众多的博物馆展览和宣传活动，激发了许多“年轻人和老年人”的想象力。与大规模建造飞行机器昆虫相关的严重固有限制需要在每一步都进行许多创新和新技术。例如，开发了一种称为弹出式MEMS的新制造工艺，以实现小规模可折叠设备的大规模生产。新的电子设备被开发出来，用来拍打昆虫大小的人造翅膀。一个新的小型计算机芯片（称为BrainSoC），连接到各种传感器，被创建来控制机器人。这项工作的高潮是令人兴奋的机器蜜蜂在精心控制的环境中盘旋和操纵的演示。这项工作的下一阶段是为这些机器人注入机器智能和自主性：RoboBee 2.0。这项计划的主要目标是教会机器人蜜蜂自主飞行。在过去的10年里，机器人专家一直忙于建造小型机器人，机器学习的活动激增，导致机器感知和控制的快速发展。例如，深度学习最近的成功可以归因于（i）更多和更高质量的数据;（ii）更快的并行计算;以及（iii）更有效的学习算法的良性循环。联合收割机将这些研究线索结合起来，为RoboBees开发机器学习飞行控制和感知的时机已经成熟。该项目汇集了来自不同工程背景的多学科专家团队，以构建下一代RoboBees。该项目旨在通过针对RoboBees平台来推动信封，该平台引入了飞行动力学和灵敏度要求，超出了使用现成组件的可能性。这项工作建立在哈佛现有的实验性RoboBee平台上，该平台配备了特殊的机载电子设备，将用于记录大量的飞行数据。然后，这些数据可以为机器学习飞行控制算法的探索提供信息，该算法从简单的悬停开始，然后再处理更具有挑战性的操作，如避障和物体跟踪。由于手工调整传统的控制算法过于繁琐，重点将放在现代计算模式，可以教，而不是编程。基于昆虫规模扑翼机器人深度学习的自主飞行控制的开发和演示将广泛影响微机器人，机器学习，节能计算和广泛的自主系统领域，进一步扩展自主能力，以广泛的机器人平台，从常规车辆到不同配置和应用的微型机器人。

项目成果

Yaw Torque Authority for a Flapping-Wing Micro-Aerial Vehicle

扑翼微型飞行器的偏航扭矩机构

• DOI: 10.1109/icra.2019.8793873
• 发表时间: 2019
• 期刊: International Conference on Robotics and Automation
• 作者: Steinmeyer, Rebecca;Hyun, Nak-seung P.;Helbling, E. Farrell;Wood, Robert J.
• 通讯作者: Wood, Robert J.

A new control framework for flapping-wing vehicles based on 3D pendulum dynamics

基于3D摆动力学的新型扑翼飞行器控制框架

• DOI: 10.1016/j.automatica.2020.109293
• 发表时间: 2021
• 期刊: Automatica
• 影响因子: 6.4
• 作者: Hyun, Nak-seung P.;McGill, Rebecca;Wood, Robert J.;Kuindersma, Scott
• 通讯作者: Kuindersma, Scott

Untethered flight of an insect-sized flapping-wing microscale aerial vehicle

• DOI: 10.1038/s41586-019-1322-0
• 发表时间: 2019-06-27
• 期刊: NATURE
• 影响因子: 64.8
• 作者: Jafferis, Noah T.;Helbling, E. Farrell;Wood, Robert J.
• 通讯作者: Wood, Robert J.

Direct Model Reference Adaptive Control for Tracking Contracting Nonlinear Systems

• DOI: 10.1109/cdc42340.2020.9303884
• 发表时间: 2020-12
• 期刊: 2020 59th IEEE Conference on Decision and Control (CDC)
• 作者: N. P. Hyun;M. Petersen;R. Wood