TY - GEN
T1 - AI-based Simultaneous Audio Localization and Communication for Robots
AU - Mjaid, Amjad Yousef
AU - Prasad, Venkatesha
AU - Jonker, Mees
AU - Van Der Horst, Casper
AU - De Groot, Lucan
AU - Narayana, Sujay
PY - 2023
Y1 - 2023
N2 - Introducing Chirpy, a hardware module designed for swarm robots that enables them to locate each other and communicate through audio. With the help of its deep learning module (AudioLocNet), Chirpy is capable of performing localization in challenging environments, such as those with non-line-of-sight and reverb. To support concurrent transmission, Chirpy uses orthogonal audio chirps and has an audio message frame design that balances localization accuracy and communication speed. As a result, a swarm of robots equipped with Chirpies can on-the-fly construct a path (or a potential field) to a location of interest without the need for a map, making them ideal for tasks such as search and rescue missions. Our experiments show that Chirpy can decode messages from four concurrent transmissions with a Bit Error Rate (BER) of at a distance of 250 cm, and it can communicate at Signal-to-Noise Ratios (SNRs) as low as -32 dB while maintaining ≈ 0 BER. Furthermore, AudioLocNet demonstrates high accuracy in classifying the location of a transmitter, even in adverse conditions such as non-line-of-sight and reverberant environments.
AB - Introducing Chirpy, a hardware module designed for swarm robots that enables them to locate each other and communicate through audio. With the help of its deep learning module (AudioLocNet), Chirpy is capable of performing localization in challenging environments, such as those with non-line-of-sight and reverb. To support concurrent transmission, Chirpy uses orthogonal audio chirps and has an audio message frame design that balances localization accuracy and communication speed. As a result, a swarm of robots equipped with Chirpies can on-the-fly construct a path (or a potential field) to a location of interest without the need for a map, making them ideal for tasks such as search and rescue missions. Our experiments show that Chirpy can decode messages from four concurrent transmissions with a Bit Error Rate (BER) of at a distance of 250 cm, and it can communicate at Signal-to-Noise Ratios (SNRs) as low as -32 dB while maintaining ≈ 0 BER. Furthermore, AudioLocNet demonstrates high accuracy in classifying the location of a transmitter, even in adverse conditions such as non-line-of-sight and reverberant environments.
UR - http://www.scopus.com/inward/record.url?scp=85159653092&partnerID=8YFLogxK
U2 - 10.1145/3576842.3582373
DO - 10.1145/3576842.3582373
M3 - Conference contribution
AN - SCOPUS:85159653092
T3 - ACM International Conference Proceeding Series
SP - 172
EP - 183
BT - Proceedings - 8th ACM/IEEE Conference on Internet of Things Design and Implementation, IoTDI 2023
PB - Association for Computing Machinery (ACM)
T2 - 8th ACM/IEEE Conference on Internet of Things Design and Implementation, IoTDI 2023
Y2 - 9 May 2023 through 12 May 2023
ER -