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

2009年，哈佛大学的一组研究人员领导了一个名为RoboBees的NSF计算远征项目，目的是建造一群名为RoboBees的扑翼机器人，其动机是制造和控制有效的机器昆虫所面临的多学科挑战。这项研究令人兴奋，通过无数的博物馆展品和外联活动，它激发了许多老少的想象力。与制造大规模飞行机器人昆虫相关的严格内在限制，每一步都需要许多创新和新技术。例如，开发了一种名为弹出式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。一种新的小型计算机芯片(称为BrainSoC)被创造出来，连接到各种传感器，以控制机器人。这项工作的高潮是令人兴奋的RoboBees在精心控制的环境中盘旋和机动的演示。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。这项提议的主要目标将是教RoboBees自主飞行。在过去的10年里，当机器人学家忙于制造小型机器人时，机器学习的活动激增，导致机器感知和控制方面的快速进步。例如，最近深度学习的成功可以归因于(I)更多和更高质量的数据；(Ii)更快的并行计算；(Iii)更高效的学习算法的良性循环。现在是时候结合这些研究思路，为RoboBees开发支持机器学习的飞行控制和感知了。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。该项目试图通过瞄准RoboBees平台来挑战极限，RoboBees平台引入了超出使用现成组件可能的前沿的飞行动力学和灵敏度要求。这项工作建立在哈佛大学现有的实验RoboBee平台上，该平台配备了特殊的机载电子设备，将用于记录大量飞行数据。然后，这些数据可以推动对机器学习飞行控制算法的探索，这些算法从简单的悬停开始，然后处理更具挑战性的动作，如避障和目标跟踪。由于手动调整传统控制算法过于繁琐，因此将重点放在可以教授而不是编程的现代计算范例上。昆虫规模的扑翼机器人基于深度学习的自主飞行控制的开发和演示将广泛影响微型机器人学、机器学习、节能计算和广泛的自主系统领域，进一步将自主能力扩展到广泛的机器人平台，从常规车辆到各种配置和应用的微型机器人。

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.