Smooth Sampling-Based Model Predictive Control Using Deterministic Samples
For roboticists and control engineers using sampling-based MPC, this incremental method improves trajectory smoothness without sacrificing performance.
This work addresses non-smooth control inputs in sampling-based MPC by proposing dsMPPI, which combines MPPI's exponential weighting with deterministic sampling and CEM improvements. Experiments show dsMPPI achieves smoother trajectories than state-of-the-art methods.
Sampling-based model predictive control (MPC) is effective for nonlinear systems but often produces non-smooth control inputs due to random sampling. To address this issue, we extend the model predictive path integral (MPPI) framework with deterministic sampling and improvements from cross-entropy method (CEM)--MPC, such as iterative optimization, proposing deterministic sampling MPPI (dsMPPI). This combination leverages the exponential weighting of MPPI alongside the efficiency of deterministic samples. Experiments demonstrate that dsMPPI achieves smoother trajectories compared to state-of-the-art methods.