Optimal mission path planning (MPP) for an air sampling unmanned aerial system

Gonzalez, Luis F., Lee, Dong-Seop, & Walker, Rodney A. (2009) Optimal mission path planning (MPP) for an air sampling unmanned aerial system. In Scheding, S. (Ed.) Proceedings of the 2009 Australasian Conference on Robotics & Automation, Australian Robotics & Automation Association, Sydney, N.S.W, pp. 1-9.

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This paper presents advanced optimization techniques for Mission Path Planning (MPP) of a UAS fitted with a spore trap to detect and monitor spores and plant pathogens. The UAV MPP aims to optimise the mission path planning search and monitoring of spores and plant pathogens that may allow the agricultural sector to be more competitive and more reliable. The UAV will be fitted with an air sampling or spore trap to detect and monitor spores and plant pathogens in remote areas not accessible to current stationary monitor methods. The optimal paths are computed using a Multi-Objective Evolutionary Algorithms (MOEAs). Two types of multi-objective optimisers are compared; the MOEA Non-dominated Sorting Genetic Algorithms II (NSGA-II) and Hybrid Game are implemented to produce a set of optimal collision-free trajectories in three-dimensional environment. The trajectories on a three-dimension terrain, which are generated off-line, are collision-free and are represented by using Bézier spline curves from start position to target and then target to start position or different position with altitude constraints. The efficiency of the two optimization methods is compared in terms of computational cost and design quality. Numerical results show the benefits of coupling a Hybrid-Game strategy to a MOEA for MPP tasks. The reduction of numerical cost is an important point as the faster the algorithm converges the better the algorithms is for an off-line design and for future on-line decisions of the UAV.

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ID Code: 33034
Item Type: Conference Paper
Refereed: Yes
Additional URLs:
Keywords: Air Sampling, Path Planning, Mission Planning, UAVs, Optimisation
ISBN: 9780980740400
Subjects: Australian and New Zealand Standard Research Classification > MATHEMATICAL SCIENCES (010000) > NUMERICAL AND COMPUTATIONAL MATHEMATICS (010300) > Optimisation (010303)
Australian and New Zealand Standard Research Classification > AGRICULTURAL AND VETERINARY SCIENCES (070000) > AGRICULTURE LAND AND FARM MANAGEMENT (070100) > Farm Management Rural Management and Agribusiness (070106)
Australian and New Zealand Standard Research Classification > ENGINEERING (090000) > AEROSPACE ENGINEERING (090100) > Flight Dynamics (090106)
Divisions: Past > QUT Faculties & Divisions > Faculty of Built Environment and Engineering
Copyright Owner: Copyright 2009 Australian Robotics and Automation Association Inc.
Deposited On: 06 Jul 2010 21:45
Last Modified: 11 Jul 2017 15:24

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