5 research outputs found
Advancing multi-vehicle deployments in oceanographic field experiments
Our research concerns the coordination and control of robotic vehicles for upper water-column oceanographic observations. In such an environment, operating multiple vehicles to observe dynamic oceanographic phenomena, such as ocean processes and marine life, from fronts to cetaceans, has required that we design, implement and operate software, methods and processes which can support opportunistic needs in real-world settings with substantial constraints. In this work, an approach for coordinated measurements using such platforms, which relate directly to task outcomes, is presented. We show the use and operational value of a new Artificial Intelligence based mixed-initiative system for handling multiple platforms along with the networked infrastructure support needed to conduct such operations in the open sea. We articulate the need and use of a range of middleware architectures, critical for such deployments and ground this in the context of a field experiment in open waters of the mid-Atlantic in the summer of 2015.Advancing multi-vehicle deployments in oceanographic field experimentsacceptedVersio
Autonomous 3D Exploration of Large Structures Using an UAV Equipped with a 2D LIDAR
This paper addressed the challenge of exploring large, unknown, and unstructured
industrial environments with an unmanned aerial vehicle (UAV). The resulting system combined
well-known components and techniques with a new manoeuvre to use a low-cost 2D laser to measure
a 3D structure. Our approach combined frontier-based exploration, the Lazy Theta* path planner, and
a flyby sampling manoeuvre to create a 3D map of large scenarios. One of the novelties of our system
is that all the algorithms relied on the multi-resolution of the octomap for the world representation.
We used a Hardware-in-the-Loop (HitL) simulation environment to collect accurate measurements
of the capability of the open-source system to run online and on-board the UAV in real-time. Our
approach is compared to different reference heuristics under this simulation environment showing
better performance in regards to the amount of explored space. With the proposed approach, the UAV
is able to explore 93% of the search space under 30 min, generating a path without repetition that
adjusts to the occupied space covering indoor locations, irregular structures, and suspended obstaclesUni贸n Europea Marie Sklodowska-Curie 64215Uni贸n Europea MULTIDRONE (H2020-ICT-731667)Unii贸n Europea HYFLIERS (H2020-ICT-779411
Learning excursion sets of vector-valued Gaussian random fields for autonomous ocean sampling
Improving and optimizing oceanographic sampling is a crucial task for marine
science and maritime resource management. Faced with limited resources in
understanding processes in the water-column, the combination of statistics and
autonomous systems provide new opportunities for experimental design. In this
work we develop efficient spatial sampling methods for characterizing regions
defined by simultaneous exceedances above prescribed thresholds of several
responses, with an application focus on mapping coastal ocean phenomena based
on temperature and salinity measurements. Specifically, we define a design
criterion based on uncertainty in the excursions of vector-valued Gaussian
random fields, and derive tractable expressions for the expected integrated
Bernoulli variance reduction in such a framework. We demonstrate how this
criterion can be used to prioritize sampling efforts at locations that are
ambiguous, making exploration more effective. We use simulations to study and
compare properties of the considered approaches, followed by results from field
deployments with an autonomous underwater vehicle as part of a study mapping
the boundary of a river plume. The results demonstrate the potential of
combining statistical methods and robotic platforms to effectively inform and
execute data-driven environmental sampling