Tracking and Following a Tagged Leopard Shark with an Autonomous Underwater Vehicle
Article first published online: 4 MAR 2013
© 2013 Wiley Periodicals, Inc.
Journal of Field Robotics
Volume 30, Issue 3, pages 309–322, May/June 2013
How to Cite
Clark, C. M., Forney, C., Manii, E., Shinzaki, D., Gage, C., Farris, M., Lowe, C. G. and Moline, M. (2013), Tracking and Following a Tagged Leopard Shark with an Autonomous Underwater Vehicle. J. Field Robotics, 30: 309–322. doi: 10.1002/rob.21450
- Issue published online: 2 APR 2013
- Article first published online: 4 MAR 2013
- Manuscript Accepted: 17 JAN 2013
- Manuscript Received: 21 JUN 2012
- National Science Foundation. Grant Number: 1245813
This paper presents a prototype system that enables an autonomous underwater vehicle (AUV) to autonomously track and follow a shark that has been tagged with an acoustic transmitter. The AUV's onboard processor handles both real-time estimation of the shark's two-dimensional planar position, velocity, and orientation states, as well as a straightforward control scheme to drive the AUV toward the shark. The AUV is equipped with a stereo-hydrophone and receiver system that detects acoustic signals transmitted by the acoustic tag. The particular hydrophone system used here provides a measurement of relative bearing angle to the tag, but it does not provide the sign (+ or −) of the bearing angle. Estimation is accomplished using a particle filter that fuses bearing measurements over time to produce a state estimate of the tag location. The particle filter combined with a heuristic-based controller allows the system to overcome the ambiguity in the sign of the bearing angle. The state estimator and control scheme were validated by tracking both a stationary tag and a moving tag with known positions. Offline analysis of these data showed that state estimation can be improved by optimizing diffusion parameters in the prediction step of the filter, and considering signal strength of the acoustic signals in the resampling stage of the filter. These experiments revealed that state estimate errors were on the order of those obtained by current long-distance shark-tracking methods, i.e., manually driven boat-based tracking systems. Final experiments took place in SeaPlane Lagoon, Los Angeles, where a 1-m leopard shark (Triakis semifasciata) was caught, tagged, and released before being autonomously tracked and followed by the proposed AUV system for several hours. © 2013 Wiley Periodicals, Inc.