Gaussian process approximation of MPC in curvilinear coordinates with residual feedforward learning enables 5x faster real-time trajectory tracking on embedded hardware with comparable closed-loop performance.
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MoTIF uses HOSVD to separate multi-parametric unsteady flow data into modal components, applies GPR for parametric and spatial interpolation and RNN for temporal forecasting, achieving under 2% relative RMS error on laminar flow cases with varying Reynolds number and angle of attack.
RoSLAC jointly optimizes platform pose and magnetometer calibration parameters via alternating optimization to achieve accurate localization from ambient magnetic fields with low computational cost.
citing papers explorer
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Real-time Gaussian Process based Approximate Model Predictive Trajectory Tracking Control for Autonomous Vehicles
Gaussian process approximation of MPC in curvilinear coordinates with residual feedforward learning enables 5x faster real-time trajectory tracking on embedded hardware with comparable closed-loop performance.
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MoTIF: A Mode-Structured Tensor Framework for Multi-Parametric Approximation, Super-Resolution and Forecasting of Unsteady Systems
MoTIF uses HOSVD to separate multi-parametric unsteady flow data into modal components, applies GPR for parametric and spatial interpolation and RNN for temporal forecasting, achieving under 2% relative RMS error on laminar flow cases with varying Reynolds number and angle of attack.
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RoSLAC: Robust Simultaneous Localization and Calibration of Multiple Magnetometers
RoSLAC jointly optimizes platform pose and magnetometer calibration parameters via alternating optimization to achieve accurate localization from ambient magnetic fields with low computational cost.