A Fast and Safe Neuromorphic Approach for Obstacle Avoidance of Unmanned Aerial Vehicle

Obstacle avoidance is a crucial task in unmanned aerial vehicles (UAV) motion planning. The accuracy and consistency of real-time visual information affect the gener-ation of obstacle avoidance commands, raising higher safety demands for obstacle avoidance. The neuromorphic computing-based obstacle avoidance solution can address these challenges. Dynamic vision sensors (DVS) exhibit low latency, low power consumption, and high dynamic range as novel neuromorphic sensors. Spiking neural networks (SNN) also leverage the same mechanism to efficiently process asynchronous and sparse event data generated by DVS, offering latency and energy efficiency advantages. Additionally, the optimal estimation method effectively mitigates the impact of noise and interference within the system, reducing the influence of errors on the algorithm and enhancing safety. Based on these considerations, this paper proposes a fast and safe obstacle avoidance framework. DVS is used to acquire event data from the environment, and a hardware-compatible lightweight SNN is employed to extract dynamic obstacle position information from the data. Compared to baseline methods, this approach reduces latency by 85%. Furthermore, two estimation methods are used to predict the movement of obstacles, ensuring flight safety by generating different UAV obstacle avoidance actions based on confidence intervals, even in the presence of obstacle information errors and omissions.