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Numerical Potential Fields Based Multi-stage Path Planning for UTM in Dense Non-segregated Airspace

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Abstract

This paper addresses the problem of Unmanned Aircraft System Traffic Management (UTM), which refers to an air transportation system wherein unmanned aircraft systems (UAS) are used to deliver small payloads on-demand by safely maneuvering in the low-altitude known urban infrastructure inhabited by dynamic air traffic. A multi-stage framework based on numerical potential fields is proposed for establishing UTM in the non-segregated airspace. Numerical potential fields are adopted for global path planning as they provide optimum stand-off clearance from obstacles in the environment. The proposed global path planning approach is augmented with a prediction-based zone determination algorithm to minimize high-density air traffic encounters by the UAS. In the event of low-density traffic in the vicinity of the UAS, the UAS neighborhood is sectorized into octants. An octant-based local collision avoidance strategy generates force fields that divert the UAS into a vacant octant. Extensive simulations have been carried out to show the efficacy of the proposed method in dense urban settings.

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Acknowledgements

The authors acknowledge support from RBCCPS, IISc. Authors also thank the members of the AE291 group for very useful discussions.

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Authors and Affiliations

  1. Guidance, Control & Decision Systems Laboratory, Department of Aerospace Engineering, Indian Institute of Science, 560012, Bangalore, Karnataka, India

    Sajid Ahamed M A, Satya Prakash K, Shuvrangshu Jana & Debasish Ghose

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  1. Sajid Ahamed M A
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  2. Satya Prakash K
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  3. Shuvrangshu Jana
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  4. Debasish Ghose
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All authors contributed to the study conception and design. Analysis and simulations were performed by Sajid Ahamed M A. All authors read and approved the final manuscript.

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Correspondence to Sajid Ahamed M A.

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M A, S.A., K, S.P., Jana, S. et al. Numerical Potential Fields Based Multi-stage Path Planning for UTM in Dense Non-segregated Airspace. J Intell Robot Syst 109, 5 (2023). https://doi.org/10.1007/s10846-023-01916-0

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