A Low-Cost Real-Time Spiking System for Obstacle Detection based on Ultrasonic Sensors and Rate Coding

TL;DR

This paper presents a low-cost, real-time obstacle detection system for robots using ultrasonic sensors and a biologically inspired spiking neural network, demonstrating effective distance-based detection and potential for future spiking filter applications.

Contribution

It introduces a novel sound-based obstacle detection system employing a spiking neural network that closely mimics biological mechanisms, validated through empirical experiments.

Findings

Firing rate increases as obstacle distance decreases

A measurable distance threshold for detection was established

System performance aligns with biological obstacle detection principles

Abstract

Since the advent of mobile robots, obstacle detection has been a topic of great interest. It has also been a subject of study in neuroscience, where flying insects and bats could be considered two of the most interesting cases in terms of vision-based and sound-based mechanisms for obstacle detection, respectively. Currently, many studies focus on vision-based obstacle detection, but not many can be found regarding sound-based obstacle detection. This work focuses on the latter approach, which also makes use of a Spiking Neural Network to exploit the advantages of these architectures and achieve an approach closer to biology. The complete system was tested through a series of experiments that confirm the validity of the spiking architecture for obstacle detection. It is empirically demonstrated that, when the distance between the robot and the obstacle decreases, the output firing rate of the system increases in response as expected, and vice versa. Therefore, there is a direct relation between the two. Furthermore, there is a distance threshold between detectable and undetectable objects which is also empirically measured in this work. An in-depth study on how this system works at low level based on the Inter-Spike Interval concept was performed, which may be useful in the future development of applications based on spiking filters.