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.
Abstract. We present a methodology and algorithm for the reconstruction of three dimensional geometric models of ancient Maltese water storage systems, i.e. cisterns, from sonar data. This project was conducted as a part of a four week expedition on the islands of Malta and Gozo. During this expedition, investigators used underwater robot systems capable of mapping ancient underwater cisterns and tunnels. The mapping included probabilistic algorithms for constructing the maps of the sonar data and computer graphics for surface reconstruction and visualization. This paper presents the general methodology for the data acquisition and the novel application of algorithms from computer graphics for surface reconstruction to this new data setting. In addition to reconstructing the geometry of the cisterns, the visualization system includes methods to enhance the understanding of the data by visualizing water level and texture detail either through the application of real image data via projective textures or by more standard texture mapping techniques. The resulting surface reconstructions and visualizations can be used by archaeologists for educational purposes and to help understand the shape and history of such water receptacles.
State Estimation for Tracking of Tagged Sharks with an AUV Christina ForneyPresented is a method for estimating the planar position, velocity, and orientation states of a tagged shark. The method is designed for implementation on an Autonomous Underwater Vehicle (AUV) equipped with a stereo-hydrophone and receiver system that detects acoustic signals transmitted by a tag. The particular hydrophone system used here provides a measurement of relative bearing angle to the tag, but does not provide the sign (+ or -) of the bearing angle. A particle filter was used for fusing measurements over time to produce a state estimate of the tag location. The particle filter combined with an active control system allowed the system to overcome the ambiguity in the sign of the bearing angle.
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