Márcio da Silva Arantes
Jesimar da Silva Arantes
ABSTRACT
This paper proposes a hybrid method to define a path planning for unmanned aerial vehicles in a non-convex environment with uncertainties. The environment becomes non-convex by the presence of no-fly zones such as mountains, cities and airports. Due to the uncertainties related to the path planning in real situations, risk of collision can not be avoided. Therefore, the planner must take into account a lower level of risk than one tolerated by the user. The proposed hybrid method combines a multi-population genetic algorithm with visibility graph. This is done by encoding all possible paths as individuals and solving a linear programming model to define the full path to be executed by the aircraft. The hybrid method is evaluated from a set of 50 maps and compared against an exact and heuristic approaches with promising results reported.
- L. Blackmore, M. Ono, and B. C. Williams. Chance-constrained optimal path planning with obstacles. IEEE Press, 2011.Google Scholar
Digital Library
- I. Châari, A. Koubâa, S. Trigui, H. Bennaceur, A. Ammar, and K. Al-Shalfan. Smartpath: An efficient hybrid aco-ga algorithm for solving the global path planning problem of mobile robots. International Journal of Advanced Robotic Systems, 2014.Google ScholarCross Ref
- R. Clarke and L. B. Moses. The regulation of civilian drones' impacts on public safety. Computer Law & Security Review, 30(3):263 -- 285, 2014.Google ScholarCross Ref
- C. Fang. Mission-level Planning with Constraints on Risk. PhD thesis, Massachusetts Institute of Technology, 2014.Google Scholar
- P. Freeman and R. Freeland. Politics & technology: U.s. polices restricting unmanned aerial systems in agriculture. Food Policy, 49, Part 1(0):302 -- 311, 2014.Google Scholar
- I. C. A. O. ICAO. Doc 9859: Safety Management Manual (SMM). University Street, Montréal, Quebec, Canada, edition 3 edition, 2013. 251 p.Google Scholar
- K. A. D. Jong. Evolutionary computation - a unified approach. MIT Press, 2006.Google Scholar
- R. Kala. Multi-robot path planning using co-evolutionary genetic programming. Expert Systems with Applications, 39(3):3817--3831, 2012. Google ScholarDigital Library
- Y. Kuwata. Real-time Trajectory Design for Unmanned Aerial Vehicles using Receding Horizon Control. 151 p., Massachusetts Institute of Technology, 2003.Google Scholar
- H. X. Li. Kongming: A Generative Planner for Hybrid Systems with Temporally Extended Goals. 237 p., Massachusetts Institute of Technology, 2010.Google Scholar
- M. Ono, B. C. Williams, and L. Blackmore. Probabilistic planning for continuous dynamic systems under bounded risk. J. Artif. Int. Res., 46(1):511--577, Jan. 2013. Google ScholarDigital Library
- Y. V. Pehlivanoglu. A new vibrational genetic algorithm enhanced with a voronoi diagram for path planning of autonomous uav. Aerospace Science and Technology, 16(1):47 -- 55, 2012.Google ScholarCross Ref
- C. Toledo, P. Franca, A. Kimms, and R. Morabito. A multi-population genetic algorithm approach to solve the synchronized and integrated two-level lot sizing and scheduling problem. International Journal of Production Research, 47:3097--3119, 2009.Google ScholarCross Ref
- A. Tuncer and M. Yildirim. Dynamic path planning of mobile robots with improved genetic algorithm. Comput. Electr. Eng., 38(6):1564--1572, Nov. 2012. Google ScholarDigital Library
- G. Varela, P. Caamano, F. Orjales, A. Deibe, F. López-Pena, and R. J. Duro. Autonomous uav based search operations using constrained sampling evolutionary algorithms. Neurocomputing, 132(0):54 -- 67, 2014. Google ScholarDigital Library
- D. Wright. Drones: Regulatory challenges to an incipient industry. Computer Law & Security Review, 30(3):226 -- 229, 2014.Google ScholarCross Ref
- X. Zhang and H. Duan. An improved constrained differential evolution algorithm for unmanned aerial vehicle global route planning. Appl. Soft Comput, pages 270--284, 2015. Google ScholarDigital Library
Index Terms
- A Hybrid Multi-Population Genetic Algorithm for UAV Path Planning
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