RI: Small: Learning Resilient Autonomous Flight Behaviors by Exploiting Collision-tolerance

这个项目调查了一种新型的空中机器人的潜力，这种机器人利用耐撞性来减轻自主飞行中的风险和挑战。现代自主无人机努力避免与环境发生任何可能的碰撞，这一目标往往很难实现，甚至更难保证。观察动物王国，人们可以观察到另一种范式。例如，飞行的小昆虫经常与环境发生冲突，但这并不妨碍它们继续生活和任务。它们在碰撞中幸存下来，几乎没有问题。在这一事实的启发下，该项目旨在了解碰撞容忍性和敏捷导航自主性之间的相互作用。一种新型的弹性微型飞行器将最大限度地提高自主飞行的敏捷性，同时将碰撞风险和可能的碰撞影响保持在某些可接受的阈值以下，从而重新定义了小型空中机器人的“安全导航”。这种新型的弹性空中机器人将反过来在一系列现实世界的应用中具有价值，例如检查难以进入、狭窄和视觉退化的环境。这包括但不限于对地下设施的勘探，通过检修孔进入货舱等。此外，该项目还努力为高等教育和K12外展做出贡献。后一项关键目标是通过设想的研究与大学课堂之间的密切联系以及与内华达州已建立的教师和12岁以下学生外联机制的协同作用来实现的。为了实现这些目标，该项目建立在四个研究方向的基础上。首先，通过研究刚性设计和柔顺设计的不同优点和缺点，研究了一组可供选择的空中机器人耐碰撞设计。其次，它的目标是设计一种新的“专家”运动规划策略，明确建模碰撞风险及其在自主导航中的影响。对于后者，研究小组还将模拟碰撞对机器人可靠定位能力的影响。第三，项目组将在耐碰撞自主飞行的框架内研究强化学习方法的潜力。鉴于控制碰撞的动态现象的混合性质以及碰撞对飞行机器人机载定位功能的影响，该项目设想在学习导航的新方法中做出一系列贡献，这些方法通过碰撞弹性缓解定位不确定性，并具有最低的计算要求。最终，该项目的目标是促进一种新型的弹性飞行机器人系统，能够支持多种现实生活应用，如工业和地下检测。同时，它旨在将前沿研究引入本科生和研究生教育活动，并加强对K12学生及其教师的推广工作。该项目由稳健情报(RI)和既定的激励竞争研究计划(EPSCoR)联合资助。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

This project investigates the potential of a new class of aerial robots that exploit collision-tolerance to mitigate risks and challenges during autonomous flight. Modern autonomous unmanned aerial vehicles strive to avoid any possible collision with the environment, a goal which is often hard to achieve and even harder to guarantee. Looking at the animal kingdom, one can observe an alternative paradigm. Small flying insects for example often come in collision with the environment but this does not hinder them from continuing with their lives and tasks. They survive a collision with little to no problem. Inspired by this fact, this project aims to understand the interplay between collision-tolerance and autonomy for agile navigation. Redefining what constitutes “safe navigation” for a small aerial robot, a new class of resilient micro flyers will maximize agility in autonomous flight, while keeping collision risks and possible impacts of a collision below certain acceptable thresholds. This new type of resilient aerial robots will in turn be valuable in a set of real-world applications such as the inspection of hard to access, narrow and visually-degraded environments. This includes but is not limited to the exploration of underground facilities, accessing cargo tanks through manholes and more. In addition, this project strives to contribute into advanced university education and K12 outreach. The latter crucial goals are achieved by close connections between the envisioned research and university classes, and through synergies with established outreach mechanisms to teachers and K12 students in the State of Nevada. To meet these goals, this project builds on top of four research directions. First, it examines a set of alternative collision-tolerant designs for aerial robots by investigating the different advantages and disadvantages of rigid and compliant designs. Second, it aims to design a new “expert” motion planning strategy that explicitly models the risk of a collision and its effect in autonomous navigation. For the latter, the research team will also model the effect of collisions in the ability of the robot to reliably localize. Third, the project team will examine the potential of reinforcement learning methods in the framework of collision-tolerant autonomous flight. Given the hybrid nature of the dynamic phenomena governing collisions and the effect of collisions in the onboard localization functionality of a flying robot, the project envisions a set of contributions in new approaches for learning to navigate that mitigate localization uncertainty through collision resilience and have minimal computational requirements. Eventually, the project aims to facilitate a new class of resilient flying robotic systems capable of supporting multiple real-life applications such as those of industrial and underground inspection. At the same time it aims to introduce leading-edge research to both undergraduate and graduate education activities, alongside strengthening outreach efforts towards K12 students and their teachers.This project is jointly funded by the Robust Intelligence (RI) and the Established Program to Stimulate Competitive Research (EPSCoR).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.