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.

Copyright © 2016 by ASME
Your Session has timed out. Please sign back in to continue.


Burke, D. , 2010, “ System Level Airworthiness Tool: A Comprehensive Approach to Small Unmanned Aircraft System Airworthiness,” Ph.D. thesis, North Carolina State University, Raleigh, NC.
Goerzen, C. , Kong, Z. , and Mettler, B. , 2010, “ A Survey of Motion Planning Algorithms From the Perspective of Autonomous UAV Guidance,” J. Intell. Rob. Syst., 57(1–4), pp. 65–100. [CrossRef]
Mittal, S. , and Deb, K. , 2007, “ Three-Dimensional Offline Path Planning for UAVs Using Multiobjective Evolutionary Algorithms,” IEEE Congress on Evolutionary Computation, IEEE, Singapore, pp. 3195–3202.
Sanders, G. , and Ray, T. , 2007, “ Optimal Offline Path Planning of a Fixed Wing Unmanned Aerial Vehicle (UAV) Using an Evolutionary Algorithm,” IEEE Congress on Evolutionary Computation, IEEE, Singapore, pp. 4410–4416.
De Filippis, L. , Guglieri, G. , and Quagliotti, F. , 2011, “ A Minimum Risk Approach for Path Planning of UAVs,” J. Intell. Rob. Syst., 61(1–4), pp. 203–219. [CrossRef]
Bortoff, S. A. , 2000, “ Path Planning for UAVs,” 2000 American Control Conference, IEEE, Chicago, IL, Vol. 1, pp. 364–368.
Medeiros, F. L. L. , and Da Silva, J. D. S. , 2011, “ Computational Modeling for Automatic Path Planning Based on Evaluations of the Effects of Impacts of UAVs on the Ground,” J. Intell. Rob. Syst., 61(1–4), pp. 181–202. [CrossRef]
Weibel, R. E. , 2005, “ Safety Considerations for Operation of Different Classes of Unmanned Aerial Vehicles in the National Airspace System,” M.S. thesis, Massachusetts Institute of Technology, Cambridge, MA.
Lum, C. W. , and Waggoner, B. , 2011, A Risk Based Paradigm and Model for Unmanned Aerial Vehicles in the National Airspace, Infotech@Aerospace, St. Louis, MO.
Cobano, J. A. , Conde, R. , Alejo, D. , and Ollero, A. , 2011, “ Path Planning Based on Genetic Algorithms and the Monte Carlo Method to Avoid Aerial Vehicle Collisions Under Uncertainties,” IEEE International Conference on Robotics and Automation, IEEE, Shanghai, China, pp. 4429–4434.
Reinhardt, L. B. , and Pisinger, D. , 2011, “ Multi-Objective and Multi-Constrained Non-Additive Shortest Path Problems,” Comput. Oper. Res., 38(3), pp. 605–616. [CrossRef]
Lamont, G. , Slear, J. , and Melendez, K. , 2007, “ UAV Swarm Mission Planning and Routing Using Multi-Objective Evolutionary Algorithms,” IEEE Symposium on Computational Intelligence in Multicriteria Decision Making, IEEE, Honolulu, HI, pp. 10–20.
de la Cruz, J. M. , Besada-Portas, E. , Torre-Cubillo, L. , Andres-Toro, B. , and Lopez-Orozco, J. A. , 2008, “ Evolutionary Path Planner for UAVs in Realistic Environments,” 10th Annual Conference on Genetic and Evolutionary Computation, ACM, Atlanta, GA, pp. 1477–1484.
Pikaar, A. , De Jong, C. , and Weijts, J. , 2000, An Enhanced Method for the Calculation of Third Party Risk Around Large Airports: With Application to Schiphol, Nationaal Lucht-en Ruimtevaartlaboratorium, Amsterdam, The Netherlands.
Wu, P. P. , and Clothier, R. A. , 2012, “ The Development of Ground Impact Models for the Analysis of the Risks Associated With Unmanned Aircraft Operations Over Inhabited Areas,” 11th Probabilistic Safety Assessment and Management Conference (PSAM11) and the Annual European Safety and Reliability Conference (ESREL 2012), Helsinki, Finland.
Ford, A. , and McEntee, K. , 2010, “ Assessment of the Risk to Ground Population Due to an Unmanned Aircraft In-Flight Failure,” AIAA Paper No. 2010-9056.
Lum, C. , Gauksheimy, K. , Deseure, C. , Vagnersx, J. , and McGeer, T. , 2011, “ Assessing and Estimating Risk of Operating Unmanned Aerial Systems in Populated Areas,” AIAA Paper No. 2011-6918.
Stevens, B. , 2003, Aircraft Control and Simulation, Wiley, Hoboken, NJ, Chap. 3.
Stengel, R. , 2004, Flight Dynamics, Princeton University Press, Princeton, NJ, Chap. 3.
MATLAB, 2012, “ Version R2012b,” The MathWorks, Inc., Natick, MA.
Roskam, J. , 1995, Airplane Flight Dynamics and Automatic Flight Controls, DARcorporation, Lawrence, KS.
Dijkstra, E. W. , 1959, “ A Note on Two Problems in Connexion With Graphs,” Numerische Math., 1(1), pp. 269–271. [CrossRef]
MATLAB, 2012, “ Optimization Toolbox User's Guide, Version R2012b,” MathWorks, Inc., Natick, MA.
Wu, J. , and Azarm, S. , 2001, “ Metrics for Quality Assessment of a Multiobjective Design Optimization Solution Set,” ASME J. Mech. Des., 123(1), pp. 18–25. [CrossRef]


Grahic Jump Location
Fig. 2

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

Grahic Jump Location
Fig. 1

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

Grahic Jump Location
Fig. 3

Closeness against computation time for the College Park case

Grahic Jump Location
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.

Grahic Jump Location
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




Some tools below are only available to our subscribers or users with an online account.

Related Content

Customize your page view by dragging and repositioning the boxes below.

Related Journal Articles
Related eBook Content
Topic Collections

Sorry! You do not have access to this content. For assistance or to subscribe, please contact us:

  • TELEPHONE: 1-800-843-2763 (Toll-free in the USA)
  • EMAIL: asmedigitalcollection@asme.org
Sign In