Abstract
In many unmanned aerial vehicle (UAV) applications, flexible trajectory generation algorithms are required to enable high levels of autonomy for critical mission phases, such as take-off, area coverage, and landing. In this paper, we present a guidance approach which uses the improved intrinsic tau guidance theory to create spatio-temporal 4-D trajectories for a desired time-to-contact with a landing platform tracked by a visual sensor. This allows us to perform maneuvers with tunable trajectory profiles, while catering for static or non-static starting and terminating motion states. We validate our method in both simulations and real platform experiments by using rotary-wing UAVs to land on static platforms. Results show that our method achieves smooth landings within 10 cm accuracy, with easily adjustable trajectory parameters.
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Acknowledgements
This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 644227 and from the Swiss State Secretariat for Education, Research and Innovation (SERI) under contract number 15.0029. We would like to thank Marco Tranzatto and Michael Pantic for their valuable insights and platform support, and the ETH Crop Science Group for providing the testing facilities.
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Vetrella, A.R. et al. (2018). Improved Tau-Guidance and Vision-Aided Navigation for Robust Autonomous Landing of UAVs. In: Hutter, M., Siegwart, R. (eds) Field and Service Robotics. Springer Proceedings in Advanced Robotics, vol 5. Springer, Cham. https://doi.org/10.1007/978-3-319-67361-5_8
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DOI: https://doi.org/10.1007/978-3-319-67361-5_8
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