High-Confidence Trajectory Planning for Off-Road Automated Vehicles under Energy Constraints

Nathan Goulet, Beshah Ayalew, Matthew Castanier, Annette Skowronska

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Scopus citations

Abstract

For automated vehicles operating in off-road environments, there is substantial uncertainty in their energy needs and utilization. To account for this uncertainty, we propose a high-confidence global planner that obtains the path with the highest-confidence energy constraints are met. We outline a sampling-based method to approximate the energy stage cost uncertainty as a normal random variable, and then transform the uncertain optimal control problem to a deterministic one that can be solved using standard methods. We couple this with a local nominal model predictive controller that employs a dynamics model of the off-road vehicle on deformable terrains. We show through Monte-Carlo simulations that the framework is robust in the face of uncertainty in terms of energy consumption and outperforms approaches that simply plan for the minimum expected energy consumption.

Original languageEnglish
Title of host publication2023 American Control Conference, ACC 2023
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages3221-3226
Number of pages6
ISBN (Electronic)9798350328066
DOIs
StatePublished - 2023
Externally publishedYes
Event2023 American Control Conference, ACC 2023 - San Diego, United States
Duration: May 31 2023Jun 2 2023

Publication series

NameProceedings of the American Control Conference
Volume2023-May
ISSN (Print)0743-1619

Conference

Conference2023 American Control Conference, ACC 2023
Country/TerritoryUnited States
CitySan Diego
Period05/31/2306/2/23

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