Acceleration-Aware Path Planning with Waypoints

  1. Ortner, Rudolf
  2. Kurmi, Indrajit
  3. Bimber, Oliver
  4. González Aguilera, Diego ed. lit. 1
  1. 1 Universidad de Salamanca

    Universidad de Salamanca

    Salamanca, España



ISSN: 2504-446X

Year of publication: 2021

Volume: 5

Issue: 4

Pages: 143

Type: Article

DOI: 10.3390/DRONES5040143 GOOGLE SCHOLAR lock_openOpen access editor

More publications in: Drones


Cited by

  • Web of Science Cited by: 1 (08-09-2023)

JCR (Journal Impact Factor)

  • Year 2021
  • Journal Impact Factor: 5.532
  • Journal Impact Factor without self cites: 4.848
  • Article influence score: 0.89
  • Best Quartile: Q2
  • Area: REMOTE SENSING Quartile: Q2 Rank in area: 10/34 (Ranking edition: SCIE)

SCImago Journal Rank

  • Year 2021
  • SJR Journal Impact: 0.995
  • Best Quartile: Q1
  • Area: Information Systems Quartile: Q1 Rank in area: 82/380
  • Area: Computer Science Applications Quartile: Q1 Rank in area: 171/791
  • Area: Aerospace Engineering Quartile: Q1 Rank in area: 21/140
  • Area: Control and Systems Engineering Quartile: Q1 Rank in area: 66/281
  • Area: Artificial Intelligence Quartile: Q2 Rank in area: 79/286

Scopus CiteScore

  • Year 2021
  • CiteScore of the Journal : 7.2
  • Area: Aerospace Engineering Percentile: 91
  • Area: Computer Science Applications Percentile: 83
  • Area: Information Systems Percentile: 83
  • Area: Control and Systems Engineering Percentile: 82
  • Area: Artificial Intelligence Percentile: 76

Journal Citation Indicator (JCI)

  • Year 2021
  • Journal Citation Indicator (JCI): 0.94
  • Best Quartile: Q2
  • Area: REMOTE SENSING Quartile: Q2 Rank in area: 18/57


In this article we demonstrate that acceleration and deceleration of direction-turning drones at waypoints have a significant influence to path planning which is important to be considered for time-critical applications, such as drone-supported search and rescue. We present a new path planning approach that takes acceleration and deceleration into account. It follows a local gradient ascend strategy which locally minimizes turns while maximizing search probability accumulation. Our approach outperforms classic coverage-based path planning algorithms, such as spiral- and grid-search, as well as potential field methods that consider search probability distributions. We apply this method in the context of autonomous search and rescue drones and in combination with a novel synthetic aperture imaging technique, called Airborne Optical Sectioning (AOS), which removes occlusion of vegetation and forest in real-time

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