Prediction of Acoustic Communication Performance for AUVs using Gaussian Process Classification
This work addresses the challenge of coordinating cooperating AUVs by improving communication reliability, though it appears incremental as it specializes existing methods to a specific binary classification problem.
The paper tackles the problem of unreliable acoustic communication between autonomous underwater vehicles (AUVs) by proposing a method to learn a probabilistic communication map based on vehicle locations, which accounts for factors like range, noise, and multi-path effects, and is validated experimentally with AUV data.
Cooperating autonomous underwater vehicles (AUVs) often rely on acoustic communication to coordinate their actions effectively. However, the reliability of underwater acoustic communication decreases as the communication range between vehicles increases. Consequently, teams of cooperating AUVs typically make conservative assumptions about the maximum range at which they can communicate reliably. To address this limitation, we propose a novel approach that involves learning a map representing the probability of successful communication based on the locations of the transmitting and receiving vehicles. This probabilistic communication map accounts for factors such as the range between vehicles, environmental noise, and multi-path effects at a given location. In pursuit of this goal, we investigate the application of Gaussian process binary classification to generate the desired communication map. We specialize existing results to this specific binary classification problem and explore methods to incorporate uncertainty in vehicle location into the mapping process. Furthermore, we compare the prediction performance of the probability communication map generated using binary classification with that of a signal-to-noise ratio (SNR) communication map generated using Gaussian process regression. Our approach is experimentally validated using communication and navigation data collected during trials with a pair of Virginia Tech 690 AUVs.