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.

Ostovar I., Niculescu V., Muller H., Polonelli T., Magno M., Benini L. (2022). Demo Abstract: Towards Reliable Obstacle Avoidance for Nano-UAVs [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.
2022
2022 21st ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN)
501
502
Ostovar I., Niculescu V., Muller H., Polonelli T., Magno M., Benini L. (2022). Demo Abstract: Towards Reliable Obstacle Avoidance for Nano-UAVs [10.1109/IPSN54338.2022.00051].
Ostovar I.; Niculescu V.; Muller H.; Polonelli T.; Magno M.; Benini L.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11585/905780
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