Expanding autonomous ground vehicle navigation capabilities through a multi-model parameterized Koopman framework

We introduce the multi-model parameterized Koopman (MMPK) framework, a novel end-to-end data-driven modeling and control pipeline for enabling autonomous navigation in Uncrewed Ground Vehicles. MMPK builds upon the Koopman extended dynamic mode decomposition (KEDMD) algorithm, offering a flexible model- and control-adaptation in the presence of time-varying uncertainties with both ego-vehicle and operational-environment parameters. Unlike traditional methods, MMPK addresses challenges such as overfitting and reliance on a singular global model by adopting a set of pose-agnostic representations of positional data and curvature-parameterized Koopman models, thereby effectively mitigating data bias. The end-to-end unified pipeline encompasses: (i) an offline data-driven learning phase to customize the multiple curvature-parameterized Koopman models and (ii) an online model-based trajectory planning and linear Model Predictive Control (outer-loop control design) adapted to switched Koopman dynamics. The performance of the proposed pipeline is verified via simulation and experimental testing using a <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mrow> <mml:mn>1</mml:mn> <mml:mo>/</mml:mo> <mml:msup> <mml:mn>5</mml:mn> <mml:mtext>th</mml:mtext> </mml:msup> </mml:mrow> </mml:math> scale Ackermann-steered ground vehicle platform (AgileX Hunter SE) and benchmark driving profiles. Comparative evaluations demonstrate MMPK’s superior path-tracking capabilities and the effectiveness of its local planning strategy in bridging the Model-Sim-Real gap.