Low Cost Obstacle Avoidance Sonar

Low-cost obstacle avoidance sonar research focuses on developing effective and affordable sonar systems for applications like small autonomous underwater vehicles (AUVs). Current efforts concentrate on improving obstacle detection and avoidance algorithms, often employing techniques like posterior expected loss and incorporating multiple sonar beams to enhance accuracy and reduce false positives. Researchers are also exploring the use of deep learning models, including convolutional neural networks (CNNs) and graph attention networks (GATs), to process sonar data and improve target recognition and classification. This work is significant for advancing the capabilities of small, resource-constrained robotic systems operating in complex underwater environments.