Improving Performance of a Jelly-tracking Underwater Vehicle Using Recognition of Animal Motion Modes

A vision-based automatic tracking system for gelatinous animals has been developed and demonstrated under a program of joint research between the Stanford University Aerospace Robotics Lab and the Monterey Bay Aquarium Research Institute (MBARI). In field tests using MBARI's ROV Ventana in the Monterey Bay, this system has demonstrated fully autonomous closed-loop control of Ventana to track a jellyfish for periods up to 1.5 hours. In these tests, conventional PID and Sliding Mode Control laws have both been used that rely primarily on the measurement of relative position errors derived from the vision-based system. This tracking system has been designed for both ROV and AUV deployments. One difference between the logic embedded in this system and the way human pilots operate is that human pilots typically exploit their a priori knowledge of how a jellyfish moves in formulating their control commands. That is, they do not rely solely on lead information determined through differentiation. Presented here is a first step for incorporating this additional knowledge-based lead information into the automatic control system. The ultimate goal is determine if this can be used to improve the overall performance and robustness of the tracking task. A key step towards quantification of motion behavior of gelatinous animals is a reliable capability to detect motion mode changes. This paper focuses on recognition of mode changes by applying techniques in real-time computer vision and supervised machine learning in the form of a support vector machine (SVM). Methods are presented to distinguish between active and resting modes, and to detect and measure rhythmic patterns in the body motions of these animals.