Output-Sampled Model Predictive Path Integral Control (o-MPPI) for Increased Efficiency

TL;DR

This paper introduces output-sampled MPPI (o-MPPI), a novel control method that enhances efficiency by better satisfying output constraints, significantly reducing computational effort in autonomous driving tasks.

Contribution

The paper proposes o-MPPI, an output-sampling-based MPPI that improves constraint satisfaction and efficiency over standard MPPI in dynamic environments.

Findings

o-MPPI requires 20 times fewer rollouts.

o-MPPI uses 4 times smaller prediction horizon.

o-MPPI achieves similar success rates with increased efficiency.

Abstract

The success of the model predictive path integral control (MPPI) approach depends on the appropriate selection of the input distribution used for sampling. However, it can be challenging to select inputs that satisfy output constraints in dynamic environments. The main contribution of this paper is to propose an output-sampling-based MPPI (o-MPPI), which improves the ability of samples to satisfy output constraints and thereby increases MPPI efficiency. Comparative simulations and experiments of dynamic autonomous driving of bots around a track are provided to show that the proposed o-MPPI is more efficient and requires substantially (20-times) less number of rollouts and (4-times) smaller prediction horizon when compared with the standard MPPI for similar success rates. The supporting video for the paper can be found at https://youtu.be/snhlZj3l5CE.