Collaborative Research: Omnidirectional Perching on Dynamic Surfaces: Emergence of Robust Behaviors from Joint Learning of Embodied and Motor Control

该项目将赋予小型飞行器（如四轴飞行器）在任意方向的静止或移动表面上自主和通用的栖息能力，从而扩大其在侦察、检查、监视、环境监测和搜救等领域的作战能力。例如，它将使它们能够降落在随海起伏和摇摆的帆船上，搭便车到移动的地面或空中平台上充电或安全，帮助人类飞行员轻松地将无人机降落在自己选择的目标上（例如，在墙壁上，电力线上，桥下）。研究将侧重于通过一个集成的学习框架来共同设计具体化的物理智能和计算智能，以实现在大多数情况下的稳健栖息。该项目还将通过具有视觉吸引力的交互式机器人飞行和栖息实验，为K-12学生创建一个STEM教育框架，以介绍机器人、机器学习、机械设计、智能材料和飞行原理等多学科概念。此外，研究成果将被整合到本科学生的各种教育和推广模块以及一般劳动力发展中，利用新成立的美国国家科学基金会资助的宾夕法尼亚州立大学自主空中移动和传感中心（CAAMS）。本研究的目的是结合物理体现智能和计算智能的设计和学习模式，以实现小型飞行器强大的全方位着陆所需的各种动态着陆机制。物理智能将通过一种新型的起落架系统来实现，该起落架系统由一系列仿生微型机器人tarsi组成，在着陆过程中可以快速调整其顺应性。计算智能将通过以下方式实现：1)通过学习预测策略区域和相关的运动控制策略映射来启动和控制停泊角机动；2)基于视觉的光流约束导引，同时将机器人带入目标策略区域和目标着陆位置。最后，将计算智能与物理智能结合起来，通过一个两层框架，分别由a)机械设计和电机控制策略以及b)具体化和电机控制策略的联合学习组成，前者控制仿生姿态顺应性，后者控制空中机动。总之，该项目将推进机器人计算智能和物理智能之间的协同设计、集成、相互作用和权衡方面的知识，以实现新颖而强大的能力。该项目由跨部门机器人基础研究项目支持，由工程（ENG）和计算机与信息科学与工程（CISE）联合管理和资助。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。

This project will endow small aerial vehicles (e.g., quadcopters) with autonomous and universal perching capability on stationary or moving surfaces of arbitrary orientations, thereby expanding their operational capabilities in the areas of reconnaissance, inspection, surveillance, environmental monitoring, and search and rescue. For example, it will enable them to land on a sailing ship that heaves and sways with the sea, to hitchhike onto a moving ground or aerial platform for charging or safety, to assist a human-pilot to easily land a drone on self-selected targets (e.g., on walls, powerlines, and underneath a bridge). The research will focus on the co-design of embodied physical and computational intelligence through an integrated learning framework to achieve robust perching in most circumstances. The project will also create a STEM educational framework for K-12 students through visually appealing, interactive robotic flight and perching experiments to introduce multidisciplinary concepts in robotics, machine learning, mechanical design, smart materials, and flight principles. Furthermore, the research outcomes will be integrated into various educational and outreach modules for undergraduate students as well as general workforce development, leveraging the newly NSF-funded Center for Autonomous Air Mobility and Sensing (CAAMS) at Pennsylvania State University.The objective of this research is to combine the design and learning modalities of both physically embodied intelligence and computational intelligence to enable a wide range of dynamic touchdown mechanisms necessary for robust omnidirectional perching of small aerial vehicles. Physical intelligence will be achieved via a novel landing gear system with an array of bio-inspired, miniature robotic tarsi, whose compliance can be rapidly tuned on the spot during the touchdown. Computational intelligence will be achieved via 1) the initiation and control of perching angular maneuvers by learning predictive policy regions and the associated policy mapping for motor control and 2) vision based, optical-flow-constrained tau-guidance that simultaneously brings a robot into the target policy region and the target landing location. Finally, computational intelligence will be integrated with physical intelligence through a two-layered framework composed of joint learning of: a) mechanical design and motor control policies and b) embodied and motor control policies, respectively, as the former component would control the bio-inspired tarsi compliance and the latter would control the aerial maneuvers. In conclusion, this project will advance knowledge in the co-design, integration, interplay, and trade-offs between computational and physical intelligence in robots to achieve novel and robust capabilities. This project is supported by the cross-directorate Foundational Research in Robotics program, jointly managed and funded by the Directorates for Engineering (ENG) and Computer and Information Science and Engineering (CISE).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.