Autonomous Tracking of Sea Turtles based on Multibeam Imaging Sonar: Toward Robotic Observation of Marine Life

Abstract

This paper proposes a method for autonomous underwater vehicles to track sea turtles without attaching any tag to them, toward efficient and long-term observation of marine life. The method uses a multibeam imaging sonar as the main sensor to detect sea turtles. The method utilizes convolutional neural network (CNN) for detecting a sea turtle in sonar imagery. Surge and yaw movements of the vehicle are controlled to maintain the relative distance and direction to the detected target. The proposed method was implemented in the AUV HATTORI. The AUV succeeded in tracking a sea turtle in natural condition for 270 seconds in shallow sea.