Looking for Trouble: Informative Planning for Safe Trajectories with Occlusions

Gilhuly, Barry; Sadeghi, Armin; Yedmellat, Peyman; Rezaee, Kasra; Smith, Stephen L.

doi:10.1109/icra46639.2022.9811994

Cited by 8 publications

(2 citation statements)

References 23 publications

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“…Rather than hardly reasoning about inadequate measurements to extract information, we focus on exploiting the positioning ability of robotic platforms to capture better and more informative measurements. Some of these environmental effects are solved by the application of specific solutions, such as in the case of occlusions, which are commonly considered in navigation [25], [26] and manipulation [27], [28]. While we aim to model diverse and complex relations in perception models, we follow a general, synergetic approach that unifies them under the same framework and allows us to integrate them into planning to obtain the most informative viewpoints.…”

Section: B Environmental Modelingmentioning

confidence: 99%

Sweep-Your-Map: Efficient Coverage Planning for Aerial Teams in Large-Scale Environments

Morilla-Cabello

Bartolomei

Teixeira

et al. 2022

IEEE Robot. Autom. Lett.

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The efficiency of path-planning in robot navigation is crucial in tasks such as search-and-rescue and disaster surveying, but this is emphasized even more when considering multirotor aerial robots due to the limited battery and flight time. In this spirit, this work proposes an efficient, hierarchical planner to achieve comprehensive visual coverage of large-scale outdoor scenarios for small drones. Following an initial reconnaissance flight, a coarse map of the scene gets built in real-time. Then, regions of the map that were not appropriately observed are identified and grouped by a novel perception-aware clustering process that enables the generation of continuous trajectories (sweeps) to cover them efficiently. Thanks to this partitioning of the map into a set of tasks, we can generalize the planning to an arbitrary number of drones and perform a well-balanced workload distribution among them. We compare our approach against a state-of-theart method for exploration and show the advantages of our pipeline in terms of efficiency for obtaining coverage in large environments. Video -https://youtu.be/V2UIrM91oQ8

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Section: B Environmental Modelingmentioning

confidence: 99%

Sweep-Your-Map: Efficient Coverage Planning for Aerial Teams in Large-Scale Environments

Morilla-Cabello

Bartolomei

Teixeira

et al. 2022

IEEE Robot. Autom. Lett.

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“…High-dimensional categorical observations, such as the output of a neural network classifying camera images, pose a challenge when applying existing Bayesian optimization techniques to adaptive sampling. Many adaptive sampling strategies compute expected reward rollouts only over scalar observation fields [28], or at best low-dimensional categorical fields [51].…”

Section: Approximate Gaussian Processesmentioning

confidence: 99%

Inference and robotic path planning over high dimensional categorical observations

San Soucie

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Advances in marine autonomy, deep-learning, and in-situ marine sensing technology have enabled oceanographers to collect vast amounts of spatiotemporally-distributed, sparse, highdimensional categorical data. Statistical models, particularly in streaming and computationally constrained settings, have lagged behind data collection. Recent developments in topic modeling for robotics have highlighted the potential to efficiently extract meaningful relationships from categorical data, and adjust robotic path-planning based on real-time inference. This dissertation seeks to fill the gap in streaming statistical models for sparse, high-dimensional categorical data, in the context of open-ocean phytoplankton community ecology. We begin by exploring the use of existing topic modeling approaches for plankton community characterization. Topic models are compared to standard ecological techniques for dimensionality reduction. The increased fidelity and expressiveness of the topic modeling approach allows for greater resolution of plankton co-occurrence relationships. By analyzing these relationships and ocean physics in and around a retentive eddy, the source of phytoplankton variability is traced to storm-driven advection on the ocean surface. We conclude that topic models offer unique insights into the causal mechanisms underlying plankton community variability. Next, we turn our focus to the development of a streaming belief model for categorical path planning. Such a model must be capable of predicting in regions without data, and it must be able to process streaming data in a computationally efficient manner. We introduce the Gaussian Dirichlet Random Field model, a novel topic model with spatially continuous latent log-probabilities. In addition to producing a more accurate model than the state-ofthe-art in locations with data, the Gaussian Dirichlet Random Field model can interpolate and extrapolate. The model is initially presented with a batch hybrid Markov Chain-Monte Carlo inference procedure. We develop a streaming fully-variational inference approach for inference, called Streaming Gaussian Dirichlet Random Fields, which satisfies both the prediction and efficiency requirements for path planning belief models. In-silico experiments demonstrate the ability of this model to accurately map latent co-occurrence patterns. Comparisons to a standard Gaussian process on both path-planning tasks and observation mapping tasks show how the ability of Streaming Gaussian Dirichlet Random Fields to leverage additional categorical observations enables superior performance.

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Streaming Gaussian Dirichlet Random Fields for Spatial Predictions of High Dimensional Categorical Observations

Soucie,

Sosik,

Girdhar

2024

Springer Proceedings in Advanced Robotics

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Looking for Trouble: Informative Planning for Safe Trajectories with Occlusions

Cited by 8 publications

References 23 publications

Sweep-Your-Map: Efficient Coverage Planning for Aerial Teams in Large-Scale Environments

Sweep-Your-Map: Efficient Coverage Planning for Aerial Teams in Large-Scale Environments

Inference and robotic path planning over high dimensional categorical observations

Streaming Gaussian Dirichlet Random Fields for Spatial Predictions of High Dimensional Categorical Observations

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