IEEE Robot. Autom. Lett.

2023

DOI: 10.1109/lra.2023.3271508

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Active Classification of Moving Targets With Learned Control Policies

Álvaro Serra-Gómez

¹

,

Eduardo Montijano

²

,

Wendelin Böhmer

³

et al.

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B Perceptual Maps1

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Cited by 4 publications

(1 citation statement)

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“…Similar to the EKF, the way in which we introduce the perceptual factors in (8) permits using existing planners to solve Eq. ( 2) [17], [30].…”

Section: B Perceptual Mapsmentioning

confidence: 99%

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

Morilla-Cabello

¹

,

²

,

³

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

“…Similar to the EKF, the way in which we introduce the perceptual factors in (8) permits using existing planners to solve Eq. ( 2) [17], [30].…”

Section: B Perceptual Mapsmentioning

confidence: 99%

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

Morilla-Cabello

¹

,

²

,

³

et al. 2022

IEEE Robot. Autom. Lett.

View full text Add to dashboard Cite

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

Distributed multi-target tracking and active perception with mobile camera networks

Casao,

Serra-Gómez,

Murillo

et al. 2024

Computer Vision and Image Understanding

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No abstract

Perceptual Factors for Environmental Modeling in Robotic Active Perception

Morilla-Cabello,

Westheider,

Popović

et al. 2024

2024 IEEE International Conference on Robotics and Automation (ICRA)

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No abstract

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