TY - GEN
T1 - High-Confidence Trajectory Planning for Off-Road Automated Vehicles under Energy Constraints
AU - Goulet, Nathan
AU - Ayalew, Beshah
AU - Castanier, Matthew
AU - Skowronska, Annette
N1 - Publisher Copyright:
© 2023 American Automatic Control Council.
PY - 2023
Y1 - 2023
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85167824179&partnerID=8YFLogxK
U2 - 10.23919/ACC55779.2023.10156050
DO - 10.23919/ACC55779.2023.10156050
M3 - Conference contribution
AN - SCOPUS:85167824179
T3 - Proceedings of the American Control Conference
SP - 3221
EP - 3226
BT - 2023 American Control Conference, ACC 2023
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2023 American Control Conference, ACC 2023
Y2 - 31 May 2023 through 2 June 2023
ER -