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Vision-Based 2D Navigation of Unmanned Aerial Vehicles in Riverine Environments with Imitation Learning

Abstract

There have been many researchers studying how to enable unmanned aerial vehicles (UAVs) to navigate in complex and natural environments autonomously. In this paper, we develop an imitation learning framework and use it to train navigation policies for the UAV flying inside complex and GPS-denied riverine environments. The UAV relies on a forward-pointing camera to perform reactive maneuvers and navigate itself in 2D space by adapting the heading. We compare the performance of a linear regression-based controller, an end-to-end neural network controller and a variational autoencoder (VAE)-based controller trained with data aggregation method in the simulation environments. The results show that the VAE-based controller outperforms the other two controllers in both training and testing environments and is able to navigate the UAV with a longer traveling distance and a lower intervention rate from the pilots.

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Acknowledgements

The authors would like to thank all human subjects who helped with the data collection in the simulation environments. We would also like to thank Greesan Gurumurthy and Marie Cor Croz from the Cyber-Human-Physical Systems lab for their kind help with the project.

Funding

The work was supported by the Office of Naval Research (ONR) under the NEPTUNE 2.0 program (No. N00014-20-1-2268).

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Authors

Contributions

P.W., R.L., A.M., and Z.K. designed the study. R.L. created high-fidelity environments in the simulation. P.W. and A.M. developed different vision-based controllers and the imitation learning framework. P.W. and A.M. wrote the script. P.W. and Z.K. designed the experiment. P.W. coordinated the experiment and analyzed the data. All authors wrote the final manuscript.

Corresponding author

Correspondence to Zhaodan Kong.

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Wei, P., Liang, R., Michelmore, A. et al. Vision-Based 2D Navigation of Unmanned Aerial Vehicles in Riverine Environments with Imitation Learning. J Intell Robot Syst 104, 47 (2022). https://doi.org/10.1007/s10846-022-01593-5

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  • DOI: https://doi.org/10.1007/s10846-022-01593-5

Keywords

  • Imitation learning
  • Vison-based control
  • Unmanned aerial vehicle
  • Riverine environments