An evolutionary computation approach to three-dimensional path planning for unmanned aerial vehicles with tactical and kinematic constraints

Kok, Jonathan, Bruggemann, Troy S., & Gonzalez, Luis F. (2013) An evolutionary computation approach to three-dimensional path planning for unmanned aerial vehicles with tactical and kinematic constraints. In Proceedings of the 15th Australian International Aerospace Congress, Melbourne Convention Centre, Melbourne, VIC.

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Abstract

This paper presents a novel evolutionary computation approach to three-dimensional path planning for unmanned aerial vehicles (UAVs) with tactical and kinematic constraints. A genetic algorithm (GA) is modified and extended for path planning. Two GAs are seeded at the initial and final positions with a common objective to minimise their distance apart under given UAV constraints. This is accomplished by the synchronous optimisation of subsequent control vectors. The proposed evolutionary computation approach is called synchronous genetic algorithm (SGA). The sequence of control vectors generated by the SGA constitutes to a near-optimal path plan. The resulting path plan exhibits no discontinuity when transitioning from curve to straight trajectories. Experiments and results show that the paths generated by the SGA are within 2% of the optimal solution. Such a path planner when implemented on a hardware accelerator, such as field programmable gate array chips, can be used in the UAV as on-board replanner, as well as in ground station systems for assisting in high precision planning and modelling of mission scenarios.

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ID Code: 57038
Item Type: Conference Paper
Refereed: Yes
Keywords: Genetic Algorithm, UAV, Path Planning
Subjects: Australian and New Zealand Standard Research Classification > ENGINEERING (090000) > AEROSPACE ENGINEERING (090100) > Aircraft Performance and Flight Control Systems (090104)
Australian and New Zealand Standard Research Classification > ENGINEERING (090000) > ELECTRICAL AND ELECTRONIC ENGINEERING (090600) > Control Systems Robotics and Automation (090602)
Divisions: Current > Research Centres > Australian Research Centre for Aerospace Automation
Current > Schools > School of Electrical Engineering & Computer Science
Current > QUT Faculties and Divisions > Science & Engineering Faculty
Copyright Owner: Copyright 2013 Australian International Aerospace Congress
Deposited On: 07 Mar 2013 02:43
Last Modified: 25 Mar 2014 17:13

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