Robust and Efficient Depth-Based Obstacle Avoidance for Autonomous Miniaturized UAVs

Nano size drones hold enormous potential to explore unknown and complex environments. Their small size makes the magile and safe for operation close to humans and allows them to navigate through narrow spaces. However, their tiny size and pay-load restrict the possibilities for onboard computation and sensing, making fully autonomous flight extremely challenging. The first step toward full autonomy is reliable obstacle avoidance, which has proven to be challenging by itself in a generic indoor environment. Current approaches utilize vision-based or 1-D sensors to support nano drone perception algorithms. This article presents a light weight obstacle avoidance system based on a novel millimeter form factor 64 pixels multizone time-of-flight (ToF) sensor and a generalized model-free control policy. In-field tests are based on the Crazy flie 2.1, extended by a custom multizone ToF deck, featuring a total flight mass of 35 g. The algorithm only uses 0.3% of theon board processing power (210 mu sexecution time) with a framerate of 15 f/s. The presented autonomous nano size drone reaches100% reliability at 0.5 m/s in a generic and previously unexploredin door environment.