CCSS: FLASH: Drone Obstacle Avoidance with Event Cameras: Bio-Inspired Architecture, Algorithm, and Platform

无人机作为快递的未来有着巨大的潜力，有可能将食品、药品、包裹和各种其他物品直接送到我们家门口。然而，实现这一愿景需要克服各种挑战，避障是一个关键组成部分。碰撞不仅会造成经济损失，而且会威胁到人身安全。这项研究项目的目标是利用一种名为事件相机的创新传感器，开发一种有效的无人机避障系统。为了实现这一目标，PI系统地研究了哺乳动物的视觉系统，并提出了一种创新的仿生信号处理架构、算法和平台，以充分释放事件相机在无人机避障方面的潜力。教育方面的努力包括发展与该提案相关的个人和团体研究项目，积极招募女性和代表性不足的少数民族学生进行研究，并通过皮特的外展项目为K-12学生提供研究机会。整个研究在三个连贯的重点中进行。推进一：开发仿生事件过滤算法和架构。推进二：开发仿生事件匹配算法。推力三：开发开源的软硬件接口。开发的解决方案将在无人机避障和仿生设计方面取得显著进步。此外，我们认为提出的仿生设计一般适用于其他时间敏感的障碍物检测和定位应用，如自动驾驶。开发的开源平台将允许移动用户和开发人员快速构建自己的想法原型，而无需从头开始构建，这降低了进入无人机相关研究的门槛。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。

Drones hold immense potential as the future of deliveries, with the possibility of transporting food, medications, packages, and various other items directly to our doorsteps. However, realizing this vision requires overcoming various challenges, and obstacle avoidance stands as a critical component. A collision will not only cause financial loss but also threaten human safety. The objective of this research project is to develop an effective drone obstacle avoidance system by leveraging an innovative sensor called event camera. To achieve this goal, the PI systematically studied the mammalian visual system and proposes an innovative bio-inspired signal processing architecture, algorithm, and platform to fully unleash the potential of event cameras for drone obstacle avoidance. The education effort includes developing both individual and group-based research projects related to this proposal, actively recruiting female and underrepresented minority students for research, and providing research opportunities for K-12 students through outreach programs at Pitt. The overall research proceeds in three coherent thrusts. Thrust I: develop bio-inspired event filtering algorithms and architecture. Thrust II: develop bio-inspired event-matching algorithms. Thrust III: develop an open-sourced hardware-software interface. The developed solutions will lead to remarkable advancements in drone obstacle avoidance and bio-inspired designs. Moreover, we believe the proposed bio-inspired designs are generally applicable to other time-sensitive obstacle detection and localization applications such as autonomous driving. The developed open-sourced platform will allow mobile users and developers to fast prototype their own ideas without the need to build from scratch, which lowers the barrier of entry to drone-related research.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.