Combining Planning and Learning for Autonomous Vehicle Navigation

AG Richards, P. Boyle

Research output: Chapter in Book/Report/Conference proceedingConference Contribution (Conference Proceeding)

7 Citations (Scopus)


This paper develops a navigation algorithm for vehicles in complex environments, combining a hard guarantee of constraint satisfaction with the ability to learn from successive missions. The method uses receding horizon control for constrained short-term path planning and control, with the cost-to-go developed by reinforcement learning. Simulation results show that the algorithm learns to reproduce the shortest path through an environment with obstacles and can determine good behaviours for multiple surveillance tasks.
Translated title of the contributionCombining Planning and Learning for Autonomous Vehicle Navigation
Original languageEnglish
Title of host publicationAIAA Guidance Navigation and Control Conference, Toronto
Publication statusPublished - Aug 2010

Bibliographical note

Conference Organiser: AIAA
Other identifier: AIAA-2010-7866


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