Demo Abstract: Towards Reliable Obstacle Avoidance for Nano-UAVs

Ostovar, Iman; Niculescu, Vlad; Muller, Hanna; Polonelli, Tommaso; Magno, Michele; Benini, Luca

doi:10.1109/ipsn54338.2022.00051

Cited by 1 publication

(1 citation statement)

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“…Some recent researches use the latest 8x8 VL53L5CX sensor, which can produce an 8x8 depth image of the observed surface. This is a unique feature for a miniature ToF sensor and has previously been implemented for the purpose of indoor navigation [28] and obstacle avoidance [29] to improve autonomous navigation, but no classification of physical objects has been done based on a depth image of this sensor.…”

Section: Related Workmentioning

confidence: 99%

Miniature Mobile Robot Detection Using an Ultralow-Resolution Time-of-Flight Sensor

Pleterski,

Škulj,

Esnault

et al. 2023

IEEE Trans. Instrum. Meas.

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Miniature mobile robots in multi-robotic systems require reliable environmental perception for successful navigation, especially when operating in the real-world environment. One of the sensors that have recently become accessible in microrobotics due to their size and cost-effectiveness is a multi-zone time-offlight (ToF) sensor. In this research, object classification using a convolutional neural network (CNN) based on an ultra-low resolution ToF sensor is implemented on a miniature mobile robot to distinguish the robot from other objects. The main contribution of this work is an accurate classification system implemented on low resolution, low processing power and low power consumption hardware. The developed system consists of a VL53L5CX ToF sensor with an 8x8 depth image and a low-power RP2040 microcontroller. The classification system is based on a customised CNN architecture to determine the presence of a miniature mobile robot within the observed terrain, primarily characterized by sand and rocks. The developed system trained on a custom dataset can detect a mobile robot with an accuracy of 91.8% when deployed on a microcontroller. The model implementation requires 7 kB of RAM, has an inference time of 34 ms, and an energy consumption during inference of 3.685 mJ.

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Section: Related Workmentioning

confidence: 99%