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


Autoria(s): Gonzalez, Luis F.; Lee, Dong-Seop; Walker, Rodney A.
Contribuinte(s)

Scheding, S.

Data(s)

2009

Resumo

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.

Formato

application/pdf

Identificador

http://eprints.qut.edu.au/33034/

Publicador

Australian Robotics & Automation Association

Relação

http://eprints.qut.edu.au/33034/1/c33034.pdf

http://www.araa.asn.au/acra/acra2009/papers/pap107s1.pdf

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.

Direitos

Copyright 2009 Australian Robotics and Automation Association Inc.

Fonte

Faculty of Built Environment and Engineering; School of Engineering Systems

Palavras-Chave #010303 Optimisation #070106 Farm Management Rural Management and Agribusiness #090106 Flight Dynamics #Air Sampling #Path Planning #Mission Planning #UAVs #Optimisation
Tipo

Conference Paper