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Learning motion primitives for planning swift maneuvers of quadrotor

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Abstract

This work proposes a novel, learning-based method to leverage navigation time performance of unmanned aerial vehicles in dense environments by planning swift maneuvers using motion primitives. In the proposed planning framework, desirable motion primitives are explored by reinforcement learning. Two-stage training composed of learning in simulations and real flights is conducted to build up a swift motion primitive library. The library is then referred in real-time and the primitives are utilized by an intelligent control authority switch mechanism when swift maneuvers are needed for particular portions of a trajectory. Since the library is constructed upon realistic Gazebo simulations and real flights together, probable modeling uncertainties which can degrade planning performance are minimal. Moreover, since the library is in the form of motion primitives, it is computationally inexpensive to be retained and used for planning as compared to solving optimal motion planning problem algebraically. Overall, the proposed method allows for exceptional, swift maneuvers and enhances navigation time performance in dense environments up to 20% as being demonstrated by real flights with Diatone FPV250 quadrotor equipped with PX4 FMU.

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Notes

  1. During training, the parameters \(u_{min}\) and \(u_{max}\) are selected as 0.2 and 2.0 m/s while spatial path segments are generated within rough limits of turning radius (\(\pm \,2.0\) m) and altitude change (\(\pm \,2.0\) m) considering our lab space limitations and Diatone FPV250 Quadrotor’s capabilities for experimental demonstration. However, agent is flexible to have different limits based on the hardware.

  2. Average deviation from the path is calculated by averaging the spatial distances between each data point of the 4th degree Bézier curve approximation of the desired path and the closest point on the 10th degree Bézier curve approximation of the resultant path to that point over the total number of data points.

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Acknowledgements

This work is financially supported by the Singapore Ministry of Education (RG185/17).

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Authors and Affiliations

  1. School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore, 639798, Singapore

    Efe Camci

  2. Department of Engineering, Aarhus University, DK-8000, Aarhus C, Denmark

    Erdal Kayacan

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  1. Efe Camci
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  2. Erdal Kayacan
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Correspondence to Erdal Kayacan.

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Camci, E., Kayacan, E. Learning motion primitives for planning swift maneuvers of quadrotor. Auton Robot 43, 1733–1745 (2019). https://doi.org/10.1007/s10514-019-09831-w

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