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Risk-Based Path Planning Optimization Methods for UAVs Over Inhabited Areas

[+] Author Affiliations
Eliot Rudnick-Cohen, Jeffrey W. Herrmann, Shapour Azarm

University of Maryland, College Park, MD

Paper No. DETC2015-47407, pp. V01AT02A004; 12 pages
doi:10.1115/DETC2015-47407
From:
  • ASME 2015 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference
  • Volume 1A: 35th Computers and Information in Engineering Conference
  • Boston, Massachusetts, USA, August 2–5, 2015
  • Conference Sponsors: Design Engineering Division, Computers and Information in Engineering Division
  • ISBN: 978-0-7918-5704-5
  • Copyright © 2015 by ASME

abstract

Operating unmanned aerial vehicles (UAVs) over inhabited areas requires mitigating the risk to persons on the ground. Because the risk depends upon the flight path, UAV operators need approaches (techniques) that can find low-risk flight paths between the mission’s start and finish points. In some areas, the flight paths with the lowest risk are excessively long and indirect because the least-populated areas are too remote. Thus, UAV operators are concerned about the tradeoff between risk and flight time. Although there exist approaches for assessing the risks associated with UAV operations, existing risk-based path planning approaches have considered other risk measures (besides the risk to persons on the ground) or simplified the risk assessment calculation. This paper presents a risk assessment technique and bi-objective optimization methods to find low-risk and time (flight path) solutions and computational experiments to evaluate the relative performance of the methods (their computation time and solution quality). The methods were a network optimization approach that constructed a graph for the problem and used that to generate initial solutions that were then improved by a local approach and a greedy approach and a fourth method that did not use the network solutions. The approaches that improved the solutions generated by the network optimization step performed better than the optimization approach that did not use the network solutions.

Copyright © 2015 by ASME

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