3,358 research outputs found
Informative Path Planning for Active Field Mapping under Localization Uncertainty
Information gathering algorithms play a key role in unlocking the potential
of robots for efficient data collection in a wide range of applications.
However, most existing strategies neglect the fundamental problem of the robot
pose uncertainty, which is an implicit requirement for creating robust,
high-quality maps. To address this issue, we introduce an informative planning
framework for active mapping that explicitly accounts for the pose uncertainty
in both the mapping and planning tasks. Our strategy exploits a Gaussian
Process (GP) model to capture a target environmental field given the
uncertainty on its inputs. For planning, we formulate a new utility function
that couples the localization and field mapping objectives in GP-based mapping
scenarios in a principled way, without relying on any manually tuned
parameters. Extensive simulations show that our approach outperforms existing
strategies, with reductions in mean pose uncertainty and map error. We also
present a proof of concept in an indoor temperature mapping scenario.Comment: 8 pages, 7 figures, submission (revised) to Robotics & Automation
Letters (and IEEE International Conference on Robotics and Automation
Informative path planning for scalar dynamic reconstruction using coregionalized Gaussian processes and a spatiotemporal kernel
The proliferation of unmanned vehicles offers many opportunities for solving
environmental sampling tasks with applications in resource monitoring and
precision agriculture. Informative path planning (IPP) includes a family of
methods which offer improvements over traditional surveying techniques for
suggesting locations for observation collection. In this work, we present a
novel solution to the IPP problem by using a coregionalized Gaussian processes
to estimate a dynamic scalar field that varies in space and time. Our method
improves previous approaches by using a composite kernel accounting for
spatiotemporal correlations and at the same time, can be readily incorporated
in existing IPP algorithms. Through extensive simulations, we show that our
novel modeling approach leads to more accurate estimations when compared with
formerly proposed methods that do not account for the temporal dimension.Comment: Accepted to IROS 202
The Hierarchic treatment of marine ecological information from spatial networks of benthic platforms
Measuring biodiversity simultaneously in different locations, at different temporal scales, and over wide spatial scales is of strategic importance for the improvement of our understanding of the functioning of marine ecosystems and for the conservation of their biodiversity. Monitoring networks of cabled observatories, along with other docked autonomous systems (e.g., Remotely Operated Vehicles [ROVs], Autonomous Underwater Vehicles [AUVs], and crawlers), are being conceived and established at a spatial scale capable of tracking energy fluxes across benthic and pelagic compartments, as well as across geographic ecotones. At the same time, optoacoustic imaging is sustaining an unprecedented expansion in marine ecological monitoring, enabling the acquisition of new biological and environmental data at an appropriate spatiotemporal scale. At this stage, one of the main problems for an effective application of these technologies is the processing, storage, and treatment of the acquired complex ecological information. Here, we provide a conceptual overview on the technological developments in the multiparametric generation, storage, and automated hierarchic treatment of biological and environmental information required to capture the spatiotemporal complexity of a marine ecosystem. In doing so, we present a pipeline of ecological data acquisition and processing in different steps and prone to automation. We also give an example of population biomass, community richness and biodiversity data computation (as indicators for ecosystem functionality) with an Internet Operated Vehicle (a mobile crawler). Finally, we discuss the software requirements for that automated data processing at the level of cyber-infrastructures with sensor calibration and control, data banking, and ingestion into large data portals.Peer ReviewedPostprint (published version
Multi-vehicle Dynamic Water Surface Monitoring
Repeated exploration of a water surface to detect objects of interest and
their subsequent monitoring is important in search-and-rescue or ocean clean-up
operations. Since the location of any detected object is dynamic, we propose to
address the combined surface exploration and monitoring of the detected objects
by modeling spatio-temporal reward states and coordinating a team of vehicles
to collect the rewards. The model characterizes the dynamics of the water
surface and enables the planner to predict future system states. The state
reward value relevant to the particular water surface cell increases over time
and is nullified by being in a sensor range of a vehicle. Thus, the proposed
multi-vehicle planning approach is to minimize the collective value of the
dynamic model reward states. The purpose is to address vehicles' motion
constraints by using model predictive control on receding horizon, thus fully
exploiting the utilized vehicles' motion capabilities. Based on the evaluation
results, the approach indicates improvement in a solution to the kinematic
orienteering problem and the team orienteering problem in the monitoring task
compared to the existing solutions. The proposed approach has been
experimentally verified, supporting its feasibility in real-world monitoring
tasks
Obstacle-aware Adaptive Informative Path Planning for UAV-based Target Search
Target search with unmanned aerial vehicles (UAVs) is relevant problem to
many scenarios, e.g., search and rescue (SaR). However, a key challenge is
planning paths for maximal search efficiency given flight time constraints. To
address this, we propose the Obstacle-aware Adaptive Informative Path Planning
(OA-IPP) algorithm for target search in cluttered environments using UAVs. Our
approach leverages a layered planning strategy using a Gaussian Process
(GP)-based model of target occupancy to generate informative paths in
continuous 3D space. Within this framework, we introduce an adaptive replanning
scheme which allows us to trade off between information gain, field coverage,
sensor performance, and collision avoidance for efficient target detection.
Extensive simulations show that our OA-IPP method performs better than
state-of-the-art planners, and we demonstrate its application in a realistic
urban SaR scenario.Comment: Paper accepted for International Conference on Robotics and
Automation (ICRA-2019) to be held at Montreal, Canad
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