Stochastic mapping for chemical plume source localization with application to autonomous hydrothermal vent discovery

Abstract

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.

(cont.) 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.