Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy at the Massachusetts Institute of Technology and the Woods Hole Oceanographic Institution February 2013Autonomous marine vehicles are increasingly used in clusters for an array of oceanographic
tasks. The effectiveness of this collaboration is often limited by communications:
throughput, latency, and ease of reconfiguration. This thesis argues that improved communication
on intelligent marine robotic agents can be gained from acting on knowledge
gained by improved awareness of the physical acoustic link and higher network layers by
the AUV’s decision making software.
This thesis presents a modular acoustic networking framework, realized through a
C++ library called goby-acomms, to provide collaborating underwater vehicles with an
efficient short-range single-hop network. goby-acomms is comprised of four components
that provide: 1) losslessly compressed encoding of short messages; 2) a set of message
queues that dynamically prioritize messages based both on overall importance and time
sensitivity; 3) Time Division Multiple Access (TDMA) Medium Access Control (MAC) with
automatic discovery; and 4) an abstract acoustic modem driver.
Building on this networking framework, two approaches that use the vehicle’s “intelligence”
to improve communications are presented. The first is a “non-disruptive”
approach which is a novel technique for using state observers in conjunction with an entropy
source encoder to enable highly compressed telemetry of autonomous underwater
vehicle (AUV) position vectors. This system was analyzed on experimental data and implemented
on a fielded vehicle. Using an adaptive probability distribution in combination
with either of two state observer models, greater than 90% compression, relative to
a 32-bit integer baseline, was achieved.
The second approach is “disruptive,” as it changes the vehicle’s course to effect an improvement
in the communications channel. A hybrid data- and model-based autonomous
environmental adaptation framework is presented which allows autonomous underwater
vehicles (AUVs) with acoustic sensors to follow a path which optimizes their ability to
maintain connectivity with an acoustic contact for optimal sensing or communication.I wish to acknowledge the sponsors of this research for their generous support
of my tuition, stipend, and research: the WHOI/MIT Joint Program, the MIT Presidential Fellowship, the Office of Naval Research (ONR) # N00014-08-1-0011, # N00014-08-1-0013, and the ONR PlusNet Program Graduate Fellowship, the Defense Advanced Research Projects Agency (DARPA) (Deep Sea Operations: Applied Physical Sciences (APS) Award # APS 11-15 3352-006, APS 11-15-3352-215 ST 2.6 and 2.7
