Multi-robot Task Assignment for Aerial Tracking with Viewpoint Constraints

Conference Paper (2021)
Author(s)

Aaron Ray (Massachusetts Institute of Technology)

Alyssa Pierson (Massachusetts Institute of Technology)

H. Zhu (TU Delft - Learning & Autonomous Control)

J. Alonso-Mora (TU Delft - Learning & Autonomous Control)

Daniela Rus (Massachusetts Institute of Technology)

Research Group
Learning & Autonomous Control
Copyright
© 2021 Aaron Ray, Alyssa Pierson, H. Zhu, J. Alonso-Mora, Daniela Rus
DOI related publication
https://doi.org/10.1109/IROS51168.2021.9636719
More Info
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Publication Year
2021
Language
English
Copyright
© 2021 Aaron Ray, Alyssa Pierson, H. Zhu, J. Alonso-Mora, Daniela Rus
Research Group
Learning & Autonomous Control
Bibliographical Note
Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.@en
Pages (from-to)
1515-1522
ISBN (print)
978-1-6654-1715-0
ISBN (electronic)
978-1-6654-1714-3
Reuse Rights

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Abstract

We address the problem of assigning a team of drones to autonomously capture a set desired shots of a dynamic target in the presence of obstacles. We present a two-stage planning pipeline that generates offline an assignment of drone to shots and locally optimizes online the viewpoint. Given desired shot parameters, the high-level planner uses a visibility heuristic to predict good times for capturing each shot and uses an Integer Linear Program to compute drone assignments. An online Model Predictive Control algorithm uses the assignments as reference to capture the shots. The algorithm is validated in hardware with a pair of drones and a remote controlled car.

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