Learning quadrotor maneuvers from optimal control and generalizing in real-time

Teodor Tomić; Moritz Maier; Sami Haddadin

doi:10.1109/ICRA.2014.6907087

Learning quadrotor maneuvers from optimal control and generalizing in real-time

Teodor Tomić, Moritz Maier, Sami Haddadin

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

27 Scopus citations

Abstract

In this paper, we present a method for learning and online generalization of maneuvers for quadrotor-type vehicles. The maneuvers are formulated as optimal control problems, which are solved using a general purpose optimal control solver. The solutions are then encoded and generalized with Dynamic Movement Primitives (DMPs). This allows for real-time generalization to new goals and in-flight modifications. An effective method for joining the generalized trajectories is implemented. We present the necessary theoretical background and error analysis of the generalization. The effectiveness of the proposed method is showcased using planar point-to-point and perching maneuvers in simulation and experiment.

Original language	English
Title of host publication	Proceedings - IEEE International Conference on Robotics and Automation
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1747-1754
Number of pages	8
ISBN (Electronic)	9781479936854, 9781479936854
DOIs	https://doi.org/10.1109/ICRA.2014.6907087
State	Published - 22 Sep 2014
Externally published	Yes
Event	2014 IEEE International Conference on Robotics and Automation, ICRA 2014 - Hong Kong, China Duration: 31 May 2014 → 7 Jun 2014

Publication series

Name	Proceedings - IEEE International Conference on Robotics and Automation
ISSN (Print)	1050-4729

Conference

Conference	2014 IEEE International Conference on Robotics and Automation, ICRA 2014
Country/Territory	China
City	Hong Kong
Period	31/05/14 → 7/06/14

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10.1109/ICRA.2014.6907087

Cite this

Tomić, T., Maier, M., & Haddadin, S. (2014). Learning quadrotor maneuvers from optimal control and generalizing in real-time. In Proceedings - IEEE International Conference on Robotics and Automation (pp. 1747-1754). Article 6907087 (Proceedings - IEEE International Conference on Robotics and Automation). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICRA.2014.6907087

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title = "Learning quadrotor maneuvers from optimal control and generalizing in real-time",

abstract = "In this paper, we present a method for learning and online generalization of maneuvers for quadrotor-type vehicles. The maneuvers are formulated as optimal control problems, which are solved using a general purpose optimal control solver. The solutions are then encoded and generalized with Dynamic Movement Primitives (DMPs). This allows for real-time generalization to new goals and in-flight modifications. An effective method for joining the generalized trajectories is implemented. We present the necessary theoretical background and error analysis of the generalization. The effectiveness of the proposed method is showcased using planar point-to-point and perching maneuvers in simulation and experiment.",

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Tomić, T, Maier, M & Haddadin, S 2014, Learning quadrotor maneuvers from optimal control and generalizing in real-time. in Proceedings - IEEE International Conference on Robotics and Automation., 6907087, Proceedings - IEEE International Conference on Robotics and Automation, Institute of Electrical and Electronics Engineers Inc., pp. 1747-1754, 2014 IEEE International Conference on Robotics and Automation, ICRA 2014, Hong Kong, China, 31/05/14. https://doi.org/10.1109/ICRA.2014.6907087

Learning quadrotor maneuvers from optimal control and generalizing in real-time. / Tomić, Teodor; Maier, Moritz; Haddadin, Sami.
Proceedings - IEEE International Conference on Robotics and Automation. Institute of Electrical and Electronics Engineers Inc., 2014. p. 1747-1754 6907087 (Proceedings - IEEE International Conference on Robotics and Automation).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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N2 - In this paper, we present a method for learning and online generalization of maneuvers for quadrotor-type vehicles. The maneuvers are formulated as optimal control problems, which are solved using a general purpose optimal control solver. The solutions are then encoded and generalized with Dynamic Movement Primitives (DMPs). This allows for real-time generalization to new goals and in-flight modifications. An effective method for joining the generalized trajectories is implemented. We present the necessary theoretical background and error analysis of the generalization. The effectiveness of the proposed method is showcased using planar point-to-point and perching maneuvers in simulation and experiment.

AB - In this paper, we present a method for learning and online generalization of maneuvers for quadrotor-type vehicles. The maneuvers are formulated as optimal control problems, which are solved using a general purpose optimal control solver. The solutions are then encoded and generalized with Dynamic Movement Primitives (DMPs). This allows for real-time generalization to new goals and in-flight modifications. An effective method for joining the generalized trajectories is implemented. We present the necessary theoretical background and error analysis of the generalization. The effectiveness of the proposed method is showcased using planar point-to-point and perching maneuvers in simulation and experiment.

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SP - 1747

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BT - Proceedings - IEEE International Conference on Robotics and Automation

PB - Institute of Electrical and Electronics Engineers Inc.

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Tomić T, Maier M, Haddadin S. Learning quadrotor maneuvers from optimal control and generalizing in real-time. In Proceedings - IEEE International Conference on Robotics and Automation. Institute of Electrical and Electronics Engineers Inc. 2014. p. 1747-1754. 6907087. (Proceedings - IEEE International Conference on Robotics and Automation). doi: 10.1109/ICRA.2014.6907087

Learning quadrotor maneuvers from optimal control and generalizing in real-time

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