Agile autonomous guidance using spatial value functions

B. Mettler, N. Dadkhah, Z. Kong

Research output: Contribution to journalArticlepeer-review

28 Scopus citations

Abstract

This paper describes an autonomous guidance system based on receding horizon (RH) optimization. The system is integrated around a spatial, state-dependent cost-to-go (SVF) function that is computed as an approximation to the value function associated with the optimal trajectory planning problem. The function captures the critical interaction between the vehicle dynamics and environment, thereby resulting in tighter coupling between planning and control. The consistency achieved between the RH optimization and the SVF enables a more rigorous implementation of the RH framework to autonomous vehicle guidance. The paper describes the overall approach along flight experimental results obtained in an Interactive Guidance and Control Laboratory.

Original languageEnglish (US)
Pages (from-to)773-788
Number of pages16
JournalControl Engineering Practice
Volume18
Issue number7
DOIs
StatePublished - Jul 2010

Bibliographical note

Funding Information:
This research was completed thanks to the partial support from Grant NNX07AN31A as part of the US Army Aeroflightdynamics Directorate (RDECOM) flight control program.

Copyright:
Copyright 2010 Elsevier B.V., All rights reserved.

Keywords

  • Autonomy
  • Cost-to-go
  • Guidance
  • Optimization
  • Trajectory
  • UAV

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