Dynamic Obstacle Avoidance for Unmanned Aerial Vehicle Using Dynamic Vision Sensor

Obstacle avoidance in dynamic environments is a critical issue in unmanned aerial vehicle (UAV) applications. Current solutions rely on deep reinforcement learning (DRL), which requires significant computing power and energy and limits UAVs with limited onboard computing resources. A combination of dynamic vision sensor (DVS) and spiking neural network (SNN) can be used for fast perception and low energy consumption. This work proposes an obstacle avoidance framework that uses DVS and SNN-based object detection algorithms to identify obstacles and a lightweight action decision algorithm to generate action commands. Simulation experiments show that the UAV can avoid 70% of dynamic obstacles, with an estimated power consumption of 1.5 to 13.5 milliwatts, and an overall delay up to 7% lower than that of reinforcement learning methods.