Fully Autonomous Neuromorphic Navigation and Dynamic Obstacle Avoidance

Xiaochen Shang, Luo Pengwei, Xinning Wang, Jiayue Zhao, Huilin Ge, Bo Dong, Xin Yang

Keywords: neuromorphic, bio-inspred algorithm, dynamic obstacle avoidance, navigation, UAV, autonomous robots, event camera

TL;DR: A fully autonomous neuromorphic pipeline for tiny autonomous UAV to navigate and avoid dynamic obstacles under various light conditions in less than 2.3 ms with energy consumption reduced to 21% of traditional architecture.

Abstract: Unmanned aerial vehicles could accurately accomplish complex navigation and obstacle avoidance tasks under external control. However, enabling unmanned aerial vehicles (UAVs) to rely solely on onboard computation and sensing for real-time navigation and dynamic obstacle avoidance remains a significant challenge due to stringent latency and energy constraints. Inspired by the efficiency of biological systems, we propose a fully neuromorphic framework achieving end-to-end obstacle avoidance during navigation with an overall latency of just 2.3 milliseconds. Specifically, our bio-inspired approach enables accurate moving object detection and avoidance without requiring target recognition or trajectory computation. Additionally, we introduce the first monocular event-based pose correction dataset with over 50,000 paired and labeled event streams. We validate our system on an autonomous quadrotor using only onboard resources, demonstrating reliable navigation and avoidance of diverse obstacles moving at speeds up to 10 m/s.

Primary Area: Neuroscience and cognitive science (e.g., neural coding, brain-computer interfaces)

Flagged For Ethics Review: true

Submission Number: 830