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

University Of Pittsburgh, Pittsburgh PA

Investigators

Abstract

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 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.