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Exploiting the Structure in Pseudospectral Methods for Trajectory Generation for Autonomous Vehicles

Author(s):

G. Ulli
Conference/Journal:

Master Thesis, SS17
Abstract:

Autonomous vehicles are becoming more and more important in various fields of engineering: from self-driving cars over drones to spacecrafts. With increasing autonomy comes the need to generate guidance trajectories on-board in an embedded computation environment. This is increasingly done with optimisation-based algorithms. Currently, the most advanced real-time optimal control tools are based on direct multiple shooting. This method struggles with problems that have a long time horizon. Pseudospectral optimal control is an alternative direct method. It is popular for offline trajectory planning in the aeronautics community, where long time horizons are common. This thesis investigates the suitability of pseudospectral methods for real-time applications. The structure of the associated nonlinear programs (NLPs) is exploited in a custom interior-point method. Nine example problems are used to demonstrate the strengths and weaknesses of pseudospectral methods when compared to multiple shooting.

Supervisors: Alexander Domahidi, Juan Jerez, Roy Smith

Year:

2017
Type of Publication:

(12)Diploma/Master Thesis
Supervisor:



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% Autogenerated BibTeX entry
@PhdThesis { Xxx:2017:IFA_5699
}
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