Enabling Obstacle Avoidance for Nano-UAVs with a multi-zone depth sensor and a model-free policy

Unmanned aerial vehicles (UAVs) are a very active research topic, and especially the nano and micro subclass, characterized by centimeter size and minimal on-board computational capabilities, have gained popularity in recent years. These lightweight platforms provide good agility and movement freedom in indoor environments, but it is still a significant challenge to enable autonomous navigation or basic obstacle avoidance capabilities using standard image sensors due to the limited computational capabilities that can be hosted on-board. This work demonstrates the possibility of using a new multi-zone Time of Flight (ToF) sensor to enhance autonomous navigation with a significantly lower computational load than most common visual-based solutions. Our system proved reliable (95%) in-field obstacle avoidance capabilities when flying in indoor environments with dynamic obstacles.
Open source project at: github.com/ETH...
Music: «Elevate» from Bensound.com
Video by: Fabrice Longchamp