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Research Papers

Risk-Based Path Planning Optimization Methods for Unmanned Aerial Vehicles Over Inhabited Areas1

[+] Author and Article Information
Eliot Rudnick-Cohen, Jeffrey W. Herrmann, Shapour Azarm

Department of Mechanical Engineering,
University of Maryland,
College Park, MD 20742

Contributed by the Design Engineering Division of ASME for publication in the JOURNAL OF COMPUTING AND INFORMATION SCIENCE IN ENGINEERING. Manuscript received June 3, 2015; final manuscript received March 22, 2016; published online April 27, 2016. Editor: Bahram Ravani.

J. Comput. Inf. Sci. Eng 16(2), 021004 (Apr 27, 2016) (7 pages) Paper No: JCISE-15-1185; doi: 10.1115/1.4033235 History: Received June 03, 2015; Revised March 22, 2016

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 that can find low-risk flight paths between the mission's start and finish points. Because the flight paths with the lowest risk could be excessively long and indirect, UAV operators are concerned about the tradeoff between risk and flight time. 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.

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Figures

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Fig. 1

Example UAV crash trajectory. The circle denotes the start point and the “×” denotes the final crash location.

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Fig. 2

Discretized heat map of crash density distribution. The scale corresponds to the probability that the vehicle will land in that cell.

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Fig. 3

Closeness against computation time for the College Park case

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Fig. 4

Selected Pareto frontier results. For the College Park case: (a) greedy approach, 30 × 12 GRID; (b) local approach, 30 × 12 GRID; (c) greedy approach, 40 × 16 GRID; and (d) local approach, 40 × 16 GRID. For PAX river case: (e) greedy approach, 40 × 16 GRID and (f) local approach, 40 × 16 GRID.

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Fig. 5

Examples of the solutions generated by the network approach with the 40 × 16 GRID (solid line) and the non-network approach with 20 waypoints (dashed line) for the College Park case: (a) wt=0,wr=1 (b) wt=0.3,wr=0.7

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