Unmanned aerial vehicles (UAVs) are a very active research topic, and especially the nano and micro subclass, characterized by cen-timeter 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.
Demo Abstract: Towards Reliable Obstacle Avoidance for Nano-UAVs / Ostovar I.; Niculescu V.; Muller H.; Polonelli T.; Magno M.; Benini L.. - ELETTRONICO. - (2022), pp. 501-502. (Intervento presentato al convegno 2022 21st ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN) tenutosi a Milano, Italy nel 04-06 May 2022) [10.1109/IPSN54338.2022.00051].
Demo Abstract: Towards Reliable Obstacle Avoidance for Nano-UAVs
Muller H.;Polonelli T.;Benini L.
2022
Abstract
Unmanned aerial vehicles (UAVs) are a very active research topic, and especially the nano and micro subclass, characterized by cen-timeter 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.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
