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

Miniature mobile robots in multirobotic systems require reliable environmental perception for successful navigation, especially when operating in a real-world environment. One of the sensors that have recently become accessible in microrobotics due to their size and cost-effectiveness is a multizone time-of-flight (ToF) sensor. In this research, object classification using a convolutional neural network (CNN) based on an ultralow-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
$8\times8$
depth image and a low-power RP2040 microcontroller. The classification system is based on a customized 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.