7 research outputs found
Autonomous surveillance for biosecurity
The global movement of people and goods has increased the risk of biosecurity
threats and their potential to incur large economic, social, and environmental
costs. Conventional manual biosecurity surveillance methods are limited by
their scalability in space and time. This article focuses on autonomous
surveillance systems, comprising sensor networks, robots, and intelligent
algorithms, and their applicability to biosecurity threats. We discuss the
spatial and temporal attributes of autonomous surveillance technologies and map
them to three broad categories of biosecurity threat: (i) vector-borne
diseases; (ii) plant pests; and (iii) aquatic pests. Our discussion reveals a
broad range of opportunities to serve biosecurity needs through autonomous
surveillance.Comment: 26 pages, Trends in Biotechnology, 3 March 2015, ISSN 0167-7799,
http://dx.doi.org/10.1016/j.tibtech.2015.01.003.
(http://www.sciencedirect.com/science/article/pii/S0167779915000190
Online Multi-Modal Learning and Adaptive Information Trajectory Planning for Autonomous Exploration
In robotic information gathering missions, scientists are typically interested in understanding variables which require proxy measurements from specialized sensor suites to estimate. However, energy and time constraints limit how often these sensors can be used in a mission. Robots are also equipped with cheaper to use navigation sensors such as cameras. In this paper, we explore a challenging planning problem in which a robot is required to learn about a scientific variable of interest in an initially unknown environment by planning informative paths and deciding when and where to use its sensors. To tackle this we present two innovations: a Bayesian generative model framework to automatically learn correlations between expensive science sensors and cheaper to use navigation sensors online, and a sampling based approach to plan for multiple sensors while handling long horizons and budget constraints. Our approach does not grow in complexity with data and is anytime making it highly applicable to field robotics. We tested our approach extensively in simulation and validated it with real data collected during the 2014 Mojave Volatiles Prospector Mission. Our planning algorithm performs statistically significantly better than myopic approaches and at least as well as a coverage-based algorithm in an initially unknown environment while having added advantages of being able to exploit prior knowledge and handle other intricacies of the real world without further algorithmic modifications
Hierarchical Probabilistic Regression for AUV-based Adaptive Sampling of Marine Phenomena
Abstract — Marine phenomena such as algal blooms can be detected using in situ measurements onboard autonomous underwater vehicles (AUVs), but understanding plankton ecology and community structure requires retrieval and analysis of water specimens. This process requires shipboard or manual sample collection, followed by onshore lab analysis which is time-consuming. Better understanding of the relationship between the observable environmental features and organism abundance would allow more precisely targeted sampling and thereby save time. In this work, we present an approach to learn and improve models that predict this relationship. Coupled with recent advances in AUV technology allowing selective retrieval of water samples, this constitutes a new paradigm in biological sampling. We use organism abundance models along with spatial models of environmental features learned immediately after AUV deployments to compute spatial distributions of organisms in the coastal ocean purely from in situ AUV data. We use Gaussian process regression along with the unscented transform to fuse the two models, obtaining both the mean and variance of the organism abundance estimates. The uncertainty in organism abundance predictions is used in a sampling strategy to selectively acquire new water specimens that improves the organism abundance models. Simulation results are presented demonstrating the advantage of performing hierarchical probabilistic regression. After the validation through simulation, we show predictions of organism abundance from models learned on lab-analyzed water sample data, and AUV survey data. I