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Multi-UAV Trajectory Optimization Utilizing a NURBS-Based Terrain Model for an Aerial Imaging Mission

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Abstract

Trajectory optimization precisely scanning an irregular terrain is a challenging problem since the trajectory optimizer needs to handle complex geometry topology, vehicle performance, and a sensor specification. To address these problems, this paper introduces a novel framework of a multi-UAV trajectory optimization method for an aerial imaging mission in an irregular terrain environment. The proposed framework consists of terrain modeling and multi-UAV trajectory optimization. The terrain modeling process employs a Non-Uniform Rational B-Spline (NURBS) surface fitting method based on point cloud information resulting from an airborne LiDAR sensor or other sensor systems. The NURBS-based surface model represents a computationally efficient terrain topology. In the trajectory optimization method, the framework introduces a multi-UAV vehicle routing problem enabling UAV to scan an entire area of interest, and obtains feasible trajectories based on given vehicle performance characteristics, and sensor specifications, and the approximated terrain model. The proposed multi-UAV trajectory optimization algorithm is tested by representative numerical simulations in a realistic aerial imaging environment, namely, San Diego and Death Valley, California.

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Acknowledgements

This paper is a major enhancement of the ICUAS 2018 accepted paper.

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Authors and Affiliations

  1. Aerospace Systems Design Laboratory, School of Aerospace Engineering, Georgia Institute of Technology North Avenue, Atlanta, GA, 30332, USA

    Youngjun Choi, Mengzhen Chen, Younghoon Choi, Simon Briceno & Dimitri Mavris

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  1. Youngjun Choi
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  2. Mengzhen Chen
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  3. Younghoon Choi
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  4. Simon Briceno
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  5. Dimitri Mavris
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Correspondence to Youngjun Choi.

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Choi, Y., Chen, M., Choi, Y. et al. Multi-UAV Trajectory Optimization Utilizing a NURBS-Based Terrain Model for an Aerial Imaging Mission. J Intell Robot Syst 97, 141–154 (2020). https://doi.org/10.1007/s10846-019-01027-9

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  • DOI: https://doi.org/10.1007/s10846-019-01027-9

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