HOME > Publications > Articles > Article Abstracts

Article Abstract – Lin et al. (2017)

Title:

A multi-autonomous underwater vehicle system for autonomous tracking of marine life

Authors and affiliations:

Yukun Lin¹, Jerry Hsiung¹, Richard Piersall², Connor White³, Christopher G. Lowe³, and Christopher M. Clark².

¹Department of Computer Science and Mathematics, Harvey Mudd College
²Department of Engineering, Harvey Mudd College
³Department of Biological Sciences, CSU Long Beach

Citation:

J. Field Robotics 34: 757–774. (2017)

Abstract:

This paper presents a multi-autonomous underwater vehicle system capable of cooperatively and autonomously tracking and following marine targets (i.e., fish) tagged with an acoustic transmitter. The AUVs have been equipped with stereo-hydrophones that receive signals broadcasted by the acoustic transmitter tags to enable real-time calculation of bearing-to-tag and distance-to-tag measurements. These measurements are shared between AUVs via acoustic modem and fused within each AUV's particle filter for estimating the target's position. The AUVs use a leader/follower multi-AUV control system to enable the AUVs to drive toward the estimated target state by following collision-free paths. Once within the local area of the target, the AUVs circumnavigate the target state until it moves to another area. The system builds on previous work by incorporating a new SmartTag package that can be attached to an individual's dorsal fin. The SmartTag houses a full inertial measurement unit (INU), video logger, acoustic transmitter, and timed release mechanism. After real-time AUV tracking experiments, the SmartTag is recovered. Logged IMU data are fused with logged AUV-obtained acoustic tag measurements within a particle filter to improve state estimation accuracy. This improvement is validated through a series of multi-AUV shark and boat tracking experiments conducted at Santa Catalina Island, California. When compared with previous work that did not use the SmartTag package, results demonstrated a decrease in mean position estimation error of 25–75%, tag orientation estimation errors dropped from 80° to 30°, the sensitivity of mean position error with respect to distance to the tag was less by a factor of 50, and the sensitivity of mean position error with respect to acoustic signal reception frequency to the tag was 25 times less. These statistics demonstrate a large improvement in the system's robustness when the SmartTag package is used.

Full text:

HTML (subscription required) | PDF (from Clark lab)