Publication Date:
2022-05-25
Description:
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 2007
Description:
This thesis presents a stochastic mapping framework for autonomous robotic chemical plume source
localization in environments with multiple sources. Potential applications for robotic chemical plume
source localization include pollution and environmental monitoring, chemical plant safety, search
and rescue, anti-terrorism, narcotics control, explosive ordinance removal, and hydrothermal vent
prospecting. Turbulent flows make the spatial relationship between the detectable manifestation of
a chemical plume source, the plume itself, and the location of its source inherently uncertain. Search
domains with multiple sources compound this uncertainty because the number of sources as well as
their locations is unknown a priori.
Our framework for stochastic mapping is an adaptation of occupancy grid mapping where the
binary state of map nodes is redefined to denote either the presence (occupancy) or absence of
an active plume source. A key characteristic of the chemical plume source localization problem
is that only a few sources are expected in the search domain. The occupancy grid framework
allows for both plume detections and non-detections to inform the estimated state of grid nodes
in the map, thereby explicitly representing explored but empty portions of the domain as well as
probable source locations. However, sparsity in the expected number of occupied grid nodes strongly
violates a critical conditional independence assumption required by the standard Bayesian recursive
map update rule. While that assumption makes for a computationally attractive algorithm, in our
application it results in occupancy grid maps that are grossly inconsistent with the assumption of
a small number of occupied cells. To overcome this limitation, several alternative occupancy grid
update algorithms are presented, including an exact solution that is computationally tractable for
small numbers of detections and an approximate recursive algorithm with improved performance
relative to the standard algorithm but equivalent computational cost.
Application to hydrothermal plume data collected by the autonomous underwater vehicle ABE
during vent prospecting operations in both the Pacific and Atlantic oceans verifies the utility of
the approach. The resulting maps enable nested surveys for homing-in on seafloor vent sites to be
carried out autonomously. This eliminates inter-dive processing, recharging of batteries, and time
spent deploying and recovering the vehicle that would otherwise be necessary with survey design
directed by human operators.
Description:
This work was funded by an NSF graduate fellowship, a fellowship from the MIT
Mechanical Engineering Department, WHOI Academic Programs
Keywords:
Stochastic analysis
;
Environmental monitoring
Repository Name:
Woods Hole Open Access Server
Type:
Thesis
Format:
application/pdf
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